The research deals with the novel application of Support Vector Machines (Support Vector Classification and Support Vector Regression) for the analysis and modelling of spatial environmental data. Multiclass classification of soil types and pollution mapping are considered as real case studies. Geostatistical tools (variography) are used to control the performance of the machines.