Water quality in urban rivers is a product of the interactions of human activities and natural processes. To explore water quality characteristics and to assess the impacts of natural and anthropogenic processes on urban river systems, we used multivariate statistical techniques to analyse water quality of a typical urban river in eastern China. Cluster analysis grouped the sites into four clusters which were affected by wastewater treatment plant effluent, untreated domestic sewage, tributaries and shipping, respectively. Cluster analysis provided scientific basis for optimizing the monitoring scheme. Three latent factors obtained from principal component analysis/factor analysis were interpreted as wastewater treatment plant effluent, untreated domestic sewage and surface runoff. Absolute principal component analysis indicated that most of the total dissolved phosphorus, nitrite, total dissolved nitrogen, and total nitrogen, Na, K and Cl resulted from the wastewater treatment plant effluent, most of the ammonia, dissolved organic carbon, sulfate and Mg resulted from the surface runoff. The pollution control measures for nitrogen and phosphorus were proposed based on the source apportionment results. The present study showed that the multivariate statistical methods are effective to identify the main pollution sources, quantify their relative contributions and provide useful water management suggesitions in urban rivers.