Anthropometry is a potential tool in estimating body composition indicators and assist in understanding human physical variations in terms of their long-range utility in understanding the body growth. The present study focused on factorial analysis of anthropometric data collected on a population to explore the possibility of clustering of body dimension data as body composition indicators. This study was carried on rural male population of Orissa, India. 26 anthropometric parameters comprising of lengths, breadths, circumferences and skinfold thicknesses were measured. The variables were treated for PCA, which generated three principal components – volume indicator, body length indicator and body fat indicator, explaining 79.5% cumulative variance of the total parameters. Split analysis of subsets of the sample showed same pattern of result as of for the analysis using the full sample. Internal data reliability test (Cronbach’s Alpha) of the sample as well as individual variables was above 0.9. Applying PCA, the study sub-grouped the anthropometric parameters under three clusters as volume indicator (breadths and circumferences on the transverse plane), body length indicator (lengths on the coronal plane) and body fat indicator (skinfold thicknesses). The data provided in this study indicate that the parameters are generalizable to the population represented by this data set for male population.