In this paper, classification of a large hydrochemical data set (more than 600 water samples and 11 hydrochemical variables) from southeastern California by fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques is performed and its application to hydrochemical facies delineation is discussed. Results from both FCM and HCA clustering produced cluster centers (prototypes) that can be used to identify the physical and chemical processes creating the variations in the water chemistries. There are several advantages to FCM, and it is concluded that FCM, as an exploratory data analysis technique, is potentially useful in establishing hydrochemical facies distribution and may provide a better tool than HCA for clustering large data sets when overlapping or continuous clusters exist.