A detailed comparison and assessment of the performance of features extracted from space-borne interferometric SAR data and classified with different types of classifiers is presented. Multi-seasonal ERS-1 and ERS-2 SAR data of the Czech Republic is used to automatically classify into four different land-cover classes. An exhaustive search in the space of all possible feature subsets out of an overall number of 14 features taken from local statistics, fractal analysis and co-occurrence matrices is presented. The evaluation of the subset performance is compared using the Jeffreys-Matusita distance in the feature space and classification performance measured on a validation set independent from the classifier's training set. Classifiers investigated are maximum-likelihood, fuzzy ARTMAP and multilayer perceptron. The exhaustive search shows the importance and irrelevance of individual features depending on the classifier used. Furthermore, the size of the best subsets ranges from three to six features only, thus decreasing overall computation time. The classifier performance is assessed by measuring overall accuracy and tau statistics. The overall classification accuracy of 88.8% for the maximum-likelihood method and 91.35% for the multilayer perceptron on the validation set is further improved to 90.9% by use of a simple Bayesian context classifier which operates on class likelihoods or to 93.2% by operating on multilayer perceptron outputs.