The analysis and interpretation the spatiotemporal patterns of river water quality are a critical element in the assessment, restoration, and protection of local and region water quality. In this case study, multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), had been integrated to evaluate and interpret spatiotemporal variations of water quality in Xiangxi River, with a 5-years (2002-2006) continual monitoring data (14 parameters at 12 sites). Hierarchical cluster analysis revealed all sites could be grouped into three clusters representing different levels of pollution: relatively less polluted upper catchments sites (US), medium polluted Middle catchments sites (MS), and highly polluted lower catchments sites (LS). Factor analysis/principal component analysis was used to explore the most important factors determining the spatiotemporal dynamics of water quality in Xiangxi River. Varifactors obtained from the factor analysis indicated the parameters responsible for water quality variation were mainly related to soluble salts (natural), point source pollution of phosphorus and non-point pollution of nitrogen (anthropogenic). Discriminant analysis provided an important data reduction as it uses six parameters (TN. Si0(2), hardness, Ca2+, WT and pH), affording 70.5% correct assignations in temporal analysis, and two parameters (NO3-N and Alk), affording 55.9% correct assignations in spatial analysis, of three different regions in the basin. The low correct assignation in spatial analysis was related to the anthropogenic influence. This study suggested that multivariate statistical techniques are useful tools for identification of important water quality monitoring sites parameters and design of a monitoring network for the effective management of water resources. (C) 2012 Elsevier Ltd and INQUA. All rights reserved.