In this study, a chlorinated-solvent contaminated groundwater site was used as the study site. Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly; it simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. The statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate groundwater quality characteristics of this site. Results show that 20 analytical items of groundwater quality of the study site are simplified into seven major representative factors through factor analysis, including ¡§background¡¨, ¡§salt residual¡¨, ¡§hardness¡¨, ¡§ethylene chloride¡¨, ¡§alkalinity¡¨, ¡§organic pollutant¡¨, and ¡§chloroform¡¨. The factor score diagram was drawn according to the score of monitoring well on each factor and 89.6% of the variance could be obtained. This study used cluster analysis to cluster in two phrases, the groundwater quality monitoring wells were classified into seven clusters according to the similarity of monitored data nature and the differences between clusters. The groundwater quality characteristics and pollutant distributions of each cluster out this site were evaluated. The clustering result indicates that for the sixth cluster (where monitoring well SW-6 was the representative well), the average concentrations of chlorides such as 1,1-dichloroethylene, 1,1-dichloroethane, and cis-1,2-dichloroethylene were the highest among the clusters, indicating those the groundwater of nearby area might be polluted by chlorinated organic compounds. In addition, to evaluate whether the clustering of cluster analysis were appropriate or not, discriminant analysis is used to evaluate clustering accuracy, in which seven Fisher discriminant coefficient formulas that were exclusively suitable for this location were established. Then, the observed values were substituted to Fisher discriminant coefficient formula. Result shows that the monitoring well¡¦s clusters obtained from discriminant analysis were totally identical with the result of actual cluster analysis; the accuracy were 100%. After performing cross-validation analysis, the result shows that the accuracy were 80%, indicating the use of discriminant analysis (with forecasting function) to verify the clustering result of the cluster analysis was highly accurate. After analyzing the pollution condition of this site using time trend and space distribution, it were determined to conclude that trichloroethylene and 1,1-dichloroethylene were the major concerning pollutants; the pollutants appeared to be spreading on a large scale, so it was difficult to use the existing data to evaluate the pollution source. After assessing environmental medium characteristics and pollutant distribution of the site, this study suggests that the use of insitu bioremediation, which is cost-effective, can be applied as a remedial mothod.