Body indices provide a superior guide to measure the body weight and were also used as an indicator of type and function in domestic animals. The PCA analysis with orthogonal and non-orthogonal rotation analysis was performed with an objective to segregate important indicator of body indices. The data on 120 goats of 12 months and above age was recorded at breeding tract for different morphometric traits along with body weight. Body indices were calculated from measured morphometric traits following standard formulae. Mean, Standard Deviation and Coefficients of variation (CV %) of body indices were estimated. Least-squares fixed model analysis of data was carried out to study the effects of non-genetic factor location on different body indices. Correlation among of body indices were estimated. Orthogonal and non-orthogonal rotations of Principal Components in Indigenous goats of Bihar was done through the transformation of the components to approximate a simple structure. The effect of sex on body indices was found non-significant. The different body indices were estimated to be 77.91 (CpI), 1.07 (LI), 0.46 DI), 86.95 (BI), 86.06 (CI), 93.71 (Pr), 46.32 (RDT), 10.24 (DTI), 1.24 (TD), 3394.46 (AI) and 12.64 (RCTI), respectively. The standard deviation and coefficient of variation (CV) for different body indices ranged between 1.42 (FW) to 19.33 (CC); between 6.2% (BI) to 12.85% (CPI), respectively in goats of Kishanganj region. Body indices have presented low to moderate variability which give scope of their improvement with selection. The KMO measure of sampling adequacy (MSA) and Bertlett’s test of Sphericity validated phenotypic correlation among body indices. Phenotypic correlations among body indices were positive and moderate to high which gives high predictability. Four principal components for body indices traits were extracted which explained 81% variation of traits. The different principal components PC1, PC2, PC3 and PC4 has contributed variation with high positive significantly loading of different indices 29% (BI and DTI), 20% (LI and RCTI), 18% (DI, RDT and CPI) and 14% (AI and BW) respectively. The body indices BI, DTI, LI and RCTI contributed majorly in explaining its variation and identified sufficient to explain the variation of body indices. The results suggest that principal component analysis (PCA) could be used in breeding programs with reduction in the number of body indices to be recorded to explain the body conformation.