Multivariate statistical techniques were applied for evaluation of temporal/ spatial variations and interpretation of a large complex water-quality data set of Shiroud River that discharges to southern part of Caspian Sea, Iran. Totally 16 parameters of water quality were monitored during 12 months at 8 sites in mountainous, flat and estuary areas. Factor analysis (FA) results showed that the first factor explained 25.76% of the total variance [comprise of electrical conductivity (EC), total dissolved solids (TDS), total hardness, calcium ion and water temperature levels]. The second factor called water quality indicator factor explained 13.99% [comprise of silicate, dissolved oxygen (DO) and pH levels], and the third factor called phosphate pollutant factor explained 10.72% (comprise of orthophosphate and total phosphorus (TP)). Additional factors were affected by part of nutrient, flow rate and general water quality, each of them recorded variance less than 10%. Discriminate analysis (DA) gave the best results for both spatial and temporal analysis. It has provided an important data reduction as it uses only four parameters (mean river depth, DO, NH4 +, and EC). Thus, DA allowed a reduction in the dimensionality of the large data set, explaining a few indicator parameters responsible for large variations in water quality. The present study shows the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and identifies probable source components in order to explain the pollution of Shiroud River.