The report deals with a first application of Support Vector Machines to the environmental spatial data classification. The simplest problem of classification is considered: using original data develop a model for the classification of the regions to be below or above some predefined level of contamination. Thus, we pose a problem as a pattern recognition task. The report presents 1) short description of Support Vector Machines (SVM) and 2) application of the SVM for spatial (environmental and pollution ) data analysis and modelling. SVM are based on the developments of V. Vapnik's Statistical Learning Theory . The ideas of SVM are very attractive both for research and applications. It was shown that they are efficient and work well in many applications. In the present study SVM were applied to the real case studies with spatial data and compared with geostatistical methods like indicator kriging. SVMs with different kernels were applied (radial basis functions - RBF, polynomial kernels, hyperbolic tangents). The basic results have been obtained with local RBF kernels. It was shown that optimal bandwidth of kernel can be chosen by minimising testing error. Real data on sediments pollution in the Geneva Lake were used.