Abstract: Aiming at the practical difficulty of processed-based non-point model in groundwater pollution management, an artificial neural network was introduced for modeling and prediction of non-point pollution. A GIS-based Back Propagation Neural Network(BPNN) was developed for modeling groundwater NO^-₃-N concentration. Field nitrogen surplus, groundwater depth, soil sandy content at 30-60 in depth and soil organic content were included as input vectors of the BPNN. By designation of buffer zone around sampling well, the BPNN simulated NO^-₃-N concentration well and effectively captured the general trend of the spatial patterns of the NO^-₃-N concentration. The study provides a practical tool for analysis and management of groundwater nitrate pollution in North China Plain and serves as a supplement of processed-based non-point pollution.