Multivariate statistical techniques were applied to identify and assess the quality of river water. Thirty samples were collected from the River Cooum, and basic chemical parameters--such as pH, effect concentration, total dissolved solids, major cations, anions, nutrients, and trace metals--were evaluated. To evaluate chemical variation and seasonal effect on the variables, analysis of variance and box-and-whisker plots were performed. Cluster analysis was applied, and pre-monsoon and post-monsoon major and minor clusters were classified. The relations among the stations were highlighted by cluster analysis, which were represented by dendograms to categorize different levels of contamination. Cluster analysis clearly grouped stations into polluted and unpolluted regions. The analysis classified the upper part of the river course into one unpolluted cluster; the middle and lower parts of the river clustered together, reflecting the presence of pollution. Factor analysis revealed that water quality is strongly affected by anthropogenic activities, rock-water interaction, and saline water intrusion. Seasonal variations in water chemistry were clearly highlighted by both cluster and factor analysis. Factor-score diagrams were used successfully to delineate the stations under study by the contributing factors, and seasonal effects on the sample stations were identified and evaluated. These statistical approaches and results yielded useful information about water quality and can lead to better water resource management.