Monitoring of heavy metal concentrations in groundwater potentially used for drinking and irrigation is very important. This study collected groundwater samples from 78 wells in July 2012 and analyzed them for 17 heavy metals (Pb, Zn, Cr, Mn, Fe, Cu, Cd, Co, Ni, Al, As, Mo, Se, B, Ti, V, Ba). Spatial distributions of these elements were identified using three different interpolation methods [inverse distance weighing (IDW), radial basis function (RBF), and ordinary kriging (OK)]. Root mean squared error (RMSE) and mean absolute error (MAE) for cross validation were used to select the best interpolation methods for each parameter. Multivariate statistical analysis [cluster analysis (CA) and factor analysis (FA)] were used to identify similarities among sampling sites and the contribution of variables to groundwater pollution. Fe and Mn levels exceeded World Health Organization (WHO) recommended limits for drinking water in almost all of the study area, and some locations had Fe and Mn levels that exceeded Food and Agriculture Organization (FAO) guidelines for drip irrigation systems. Al, As, and Cd levels also exceeded WHO guidelines for drinking water. Cluster analysis classified groundwater in the study area into three groups, and factor analysis identified five factors that explained 73.39% of the total variation in groundwater, which are as follows: factor 1: Se, Ti, Cr, Mo; factor 2: Ni, Mn, Co, Ba; factor 3: Pb, Cd; factor 4: B, V, Fe, Cu; and factor 5: AS, Zn. As a result of this study, it could be said that interpolation methods and multivariate statistical techniques gave very useful results for the determination of the source.