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Forecasting soil pollution is an important part of environmental protection around the world. The work is devoted to the use of artificial neural networks for spatial prediction of the anomalously distributed chemical element Chromium (Cr). Generalized regression neural networks (GRNN) and multilayer perceptrons (MLP) are classes of neural networks widely used for continuous mapping of functions. Each network has its pros and cons; however, both demonstrated quick preparation and good modeling capabilities. In the paper, we studied and compared two neural networks: GRNN and MLP. The present study is based on real data sets on surface contamination of Cr at a specific location in subarctic Novy Urengoy, Russia, obtained during previous screening. The network structures were chosen during computer modeling, based on the minimization of the root mean squared error (RMSE). GRNN (anisotropy) showed the best predictive accuracy in comparison with MLP and kriging. [ABSTRACT FROM AUTHOR]