The Support Vector Machine (SVM) algorithm is assessed for the classification of polarimetric radar data for the cartography of natural vegetation. Fully polarimetric data has been acquired in L and P bands during an AIRSAR mission over the French Polynesian Island named Tubuai. The results show significant improvement when compared to those obtained with the classification based on the maximum likehood criterion applied to the theoretical Wishart distribution that are supposed a priori to be verified by radar data. Obviously, this hypothesis is not verified with the present experimental data over the study site. The addition of other polarimetric indicators to the elements of the polarimetric coherency matrix still improves the classification accuracy.