INTRODUCTION Rice (Oryza sativa L.) is the world's most extensively farmed cereal grain, providing sustenance to one-third of the world's population. India, behind China, has the world's largest rice-growing area, with 44 million hectares (Mha) under cultivation. The Kharif rice production in India during 2021–22 is estimated to be 107.04 million tonnes (Mt) (Anonymous, 2021). Rice yield increase of 1.0–1.2% per year beyond 2020 will be required to feed the world's still-growing population while keeping prices low (Anonymous, 2013). Sodicity is one of the most pressing concerns among all the abiotic issues that rice, a salt-sensitive cereal, faces during its vegetative and reproductive stages, limiting its production and productivity (Sheoran et al., 2021; Liang et al., 2015). It causes phosphorus, zinc, and potentially iron insufficiency, as well as boron toxicity. Plants are less able to acquire crucial nutrients from the soil when they are affected by sodicity (Upadhyay et al., 2020). Reduced germination rate, stunted plant growth, limited root development, low tillering, spikelet sterility and number, low test weight, low yield, uneven field growth, poor root growth, leaf rolling, low harvest index, leaf browning, delayed flowering, reduced seed set due to lower pollen viability and mortality are some of the pertinent morphological signs a plant shows when grown in sodic soil. Alkalization of soil due to NaHCO3 and Na2CO3 might be a more severe problem than soil salinisation caused by neutral salts, such as NaCl and Na2SO4, in certain respects (Li et al., 2017). Furthermore, extensive field testing revealed production losses ranging from 36 to 69% in rice grown on sodium salt-affected soils versus normal soils. (Upadhyay et al., 2022; Qadir et al., 2014). The use of sodic lands for agricultural yield improvement is anticipated to increase to meet the desired yield gain, and the long-term sustainability of sodic soils for food and feed production will become a major problem. The selection of those features that are yield determiners under sodic circumstances should be examined in this context. Plant breeders frequently measure a vast number of traits, some of which may lack adequate discriminating power for germplasm assessment, characterization, and management (Maji and Shaibu 2012). In this situation, Principal Component Analysis (PCA) can be used to uncover patterns in data sets and reduce redundancy. It is a multivariate statistical approach that attempts to simplify and evaluate the interrelationship between a large number of variables in terms of a small number of variables or components without sacrificing any of the original data set's key information. Clifford and Stephenson (1975) reported that the first three principal components are frequently the most essential in representing variation patterns. With the above argument, an experiment was conducted to evaluate the Principal Components Analysis of the traits in Recombinant Inbred Lines (RILs) obtained from a cross of PUSA 44 (Susceptible) and CSR 43 (Tolerant) cultivated under sodicity conditions. MATERIALS AND METHODS The genetic materials consisted of two parents viz., PUSA 44 (Sodicity susceptible) as female and CSR 43 (Sodicity Tolerant) as male parents. In Kharif 2016, at the Agricultural farm of the Institute of Agricultural Sciences, Banaras Hindu University (BHU), Varanasi, a cross was established between the parents, and F1s were obtained. These F1s were validated using SSR markers. Single F1 plant was selfed, and 250 RILs were generated in Kharif 2019 following off-season sowing at ICAR-National Rice Research Institute and main-season sowing at BHU utilising the Single Seed Descent (SSD) technique up to F7 generation. The phenotypic evaluation was performed in Kharif 2020 at NSP-6 Agricultural Research Farm of Acharya Narendra Dev University of Agriculture and Technology (ANDUAT), Kumarganj, Ayodhya. ANDUAT is located in the Gangetic alluvium of Eastern Uttar Pradesh, with a latitude of 26o47' North and a longitude of 81o12' East, at the height of 113 meters above mean sea level. It lies in the semi-arid zone, with the monsoon supplying 80% of total precipitation. The soil samples were taken randomly from various locations, followed by mixing in equal proportions. Later the mixture was subjected to pH and EC meters following standard protocol. Post Analysis, the location was found to have a pre-transplanting and post-harvest pH of 9.6 and 9.2, respectively, while the EC (at 25oC) and ESP were 0.65 dSm-1 and 75.57, respectively. Accordingly, the soil was classified as moderately sodic. Ten seedlings (F7) per line of 21 days old were transplanted following alpha lattice design of experiment with two replications of 20 × 20 cm spacing. Recommended package of practices were followed and data was recorded on five randomly selected plants for the quantitative traits viz., Days to 50% Flowering, Days to Maturity, Plant Height (cm), Effective Tillers per plant, Panicle Length, Filled Grains Per Panicle, Test Weight (g), SPAD and Grain Yield Per Plant (g). Principal Component Analysis was performed on the mean data collected concerning all of the aforesaid quantitative traits using STAR v.2.0.1 of IRRI. RESULT AND DISCUSSION By evaluating the dependency among the characteristics, principal component analysis (PCA) is used to reduce big data sets into smaller principal components with no loss of detail. Table 1 depicts Eigenvalues, Variance Percentage and Cumulative Percentage. Three of the eight components evaluated in the current study had Eigenvalues more than one. The components having Eigenvalue more than one together controlled 91.68 % variance among the selected traits. PC1, PC2 and PC3 controlled 75.02, 12.99 and 3.66 % variability among the RILs for the traits under study. The contributions of nine quantitative traits to the principal components are shown in Table 2. The same principal component showed an Eigenvalue of 5.752, 2.169 and 1.33, respectively. PC1 showed panicle length (0.3554) and test weight (0.3486) as the relatively higher contributors. Similar findings were reported by Manohara et al. (2020); Sahu et al. (2016). No negative relation among traits was found for PC1. The highest positively correlated variables for PC2 were Filled Grains per Panicle (0.4800) and Grain Yield per Plant (0.4195). The component PC3 had Effective Tillers per plant, and Panicle Length displayed the highest contributions. Christina et al. (2021) reported similar observations. The scree plot showed the variance percentage between Eigenvalues and the Principal Components (Fig. 1). PC1 exhibited 75.02 % variability with an Eigenvalue of 5.752. The graph depicts that maximum variation was contributed by PC1. A similar report was shown by Shivani et al. (2021).