Eigen values of six (6) principal components havebeen shown in the scree plot (Fig. 1). Principal componentanalysis was done using of six characters (Table 3). Thefirst principal component was absolutely linked to days to50% flowering, days to maturity, plant height, pods/plant.However, 100-seed weight and seed yield were negativelycorrelated. The second principal component was negativelyrelated to days to maturity and plant height, while rest ofthe character was positively correlated. The third principalcomponent was partially influenced by 100-seed weight(0.8687). Analysis of genetic distances showed the mostprominent traits influencing the seed yield. All the sixquantitative variables contributed in the total variance. Thefirst three principal components were accounted for 90%(59.76%, 18.04% and 13.19%, respectively) of the variabilitypresent in the material (Table 4). The first principalcomponent is the major source of the variation that accountfor the greatest possible variance. The proportion of totalvariation more than 75% is acceptable in this kind of studies(Cadima and Jolliffe 2001 and Jolliffee 2001). Rahim et al.(2008) got more than 71.48% of the variability amongst 34genotypes evaluated for 8 traits Siddika et al. (2014)reported that first principal component alone showed thevariation of 91.42% (While studying with twenty fiveadvanced breeding lines of vegetable pea). Ouafi et al.(2016) also showed that the principal component analysisrevealed more than 85% of variation in fieldpea. Espositoet al. (2007) also got similar results of 81 % variation infieldpea genotypes studies using PCA analysis andconcluded that this much variability in the material wassufficient for generating new gene combination for furtheryield improvement. Habtamu and Million(2013) studiedprincipal component analysis in fieldpea and found outthat among12 PCs, four were accounted for more than 89%of the total variation while in that first PC alone contributed40.26% of the total variation.