This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA)/principal component analysis (PCA) and cluster analysis (CA), were applied for the evaluation of variations and for the interpretation of a water quality data set of the natural water bodies obtained during 2010 year of monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physico-chemical parameters of natural water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78 % of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounting for 28 % of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounting for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounting 17 % of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounting 13 % of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA) is an objective technique to identify natural groupings in the set of data. CA divides a large number of objects into smaller number of homogenous groups on the basis of their correlation structure. CA combines the data objects together to form the natural groups involving objects with similar cluster properties and separates the objects with different cluster properties. CA showed similarities and dissimilarities among the sampling sites and explain the observed clustering in terms of affected conditions. Using FA/PCA and CA have been identified water bodies that are under the highest pressure. With regard to the factors identified water bodies are: for factor F1 (Plazovic, Bosut, Studva, Zlatica, Stari Begej, Krivaja), for factor F2 (Krivaja, Keres), for factor F3 (Studva, Zlatica, Tamis, Krivaja i Keres) and for factor F4 (Studva, Zlatica, Krivaja, Keres).