Feature extraction of SAR data based on eigenvector of texture samples tries to find the principle components of the distribution of training sets. These eigenvectors can be considered as a set of features, which together characterize the variations between training samples for each class. Defining covariance matrix is also an important issue to achieve significant classification accuracy. In this study, classification is performed based on eigenvectors of textures and way level cooccurrence matrix. Both statistical based decision rules and neural networks are applied as a classifier to test the performance of the feature extraction method based on eigenvector, of texture samples and cooccurrence matrix.