Groundwater quality is the critical factor that affects human health and the quality of industrial products in Foshan City, South China. Multivariate statistical techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), were applied to evaluate and interpret the complex groundwater quality in eastern Chancheng district, Foshan City. During the dry and wet seasons, 60% and 11% of the total groundwater samples (respectively) are suitable for drinking purposes; other samples can be used for drinking after being treated for pH, Fe, Mn, Al, NH4, and NO3. Similarly, during the dry and wet seasons, 75% and 33% of the total groundwater samples (respectively) are suitable for industrial purposes; other samples can be used for industrial purposes after being treated for NH4 and NO3. Five principal components are extracted from PCA and used to explain 81.78% of the variance in groundwater. The indicators to groundwater quality assessment are EC, Na, Cl, Fe, Mn, NH4, pH, Eh, PO4, HCO3, and K from PCA. HCA reveals that groundwater samples in the study area can be classified into three groups: one reflecting the interaction of groundwater and sediment medium along with the role of cation exchange; another reflecting the role of anion exchange between phosphate and carbonate; and the final reflecting the reducing environment.