Groundwater resources can be potentially a target for various sources of contaminations. One major step towards characterization of contamination sources and the associated parameters, is to conduct groundwater quality assessment through different methods. When dealing with heavy metals, calculation of pollution indices is among well-known techniques of contamination characterization. The objectives of this study include primary assessment of physicochemical parameters of the groundwater and heavy metal concentrations and finding distributions using multivariate statistical methods in the study area (i.e. north Isfahan province, Iran). Heavy metal pollution index (HPI) and metal index (MI) were the two indices evaluated for contamination assessment of heavy metals in 35 samples drawn within the study area. Generally, results indicated that the HPI of the groundwater in the study area was less than the critical value (< 100). Based on HPI values, 51% of samples were classified as having low pollution levels, 46% medium pollution levels and 3% high pollution levels. The MI results revealed that two samples in the study area were significantly polluted with heavy metals. Water-type classification according to dominant cations and anions was also conducted and the result identified four types of water: Na–Cl, Na–SO4, Na–HCO3 and Ca–HCO3. Multivariate statistical techniques [namely, correlation analysis and principal component analysis (PCA)] were applied for the evaluation of variations and interpretation of a large complex groundwater quality data set from the study area. The principal component analysis (PCA) extracted four components that control the groundwater chemistry. Findings of our study can be used in devising preventive measures to control pollution in the study area and similar regions where the groundwater resource would be relied upon for drinking purposes in the future.