Abstract: Understanding the most effective pollutants affecting groundwater quality is of utmost importance in promoting sustainable development of groundwater resource. The study was performed to reduce the less significant parameter and give a preliminary judgment on the most significant water quality parameters discriminating the groundwater regions based on ANN model. This, study shows the use of sensitivity analysis combined with environmetric techniques such as Cluster Analysis (CA), Discriminant Analysis (DA). The water quality data was obtained from 10 different wells, over the period of 6 years (2006-2011) using 24 water quality parameters. Sensitivity analysis was carried out for nine models (ANN-R-AP, ANN-R-Na⁺, ANN-R-Ca⁺, ANN-R-HCO₃, ANN-R-Cl-, ANN-R-SiO₂, ANN-R-TDS, ANN-R-pH, ANN-R-EC). Percentage of contribution and R² was used for model performance evaluation criterion. The CA allowed the formation of two clusters between the sampling wells. The Low Contaminant Level as LCL and moderate contaminant level as MCL reflecting differences on water quality at different locations. DA as a data reduction techniques was used to evaluate the spatial variability in water quality as it uses 6 parameters (SO₄^-,Cl^-, As, Mn, NO₂ and total dissolved solid) affording 90.00% correct assignation to discriminate between the clusters using forward stepwise mode from the original 24 parameters. The sensitivity analysis reveals that Na⁺, HCO₃, SiO₂ and EC are the four most effective parameters for discriminating groundwater quality regions with a percentage of contribution of 17.49, 17.50, 17.57 and 17.46%, respectively. This study reveals the significance of sensitivity analysis and multivariate techniques for the use of less parameter for understanding the most effective pollutant in water resource management, since, its time and cost consuming.