Multivariate statistical techniques, such as cluster analysis and principal component analysis (PCA), were applied for evaluation of spatial variations and interpretation of large complex water quality data set of the Ganga river basin, generated during one year (2013-2014) monitoring of eight water parameters at seven different sites. Hierarchical cluster analysis grouped seven sampling sites into three clusters, i.e., relatively low polluted (LP), medium polluted (MP) and highly polluted (HP) sites based on the similarity of water quality characteristics.