Classification of Polarimetric SAR (PolSAR) imagery is still one of the challenges in active remote sensing applications. Although a large number of features and different classifiers have been proposed, no unique approach has been found to satisfy all of the images and classes yet. In this paper, given the extracted features from PolSAR data, a new class-based feature selection (CBFS) algorithm is proposed to find the most optimum features for each class. Maximizing the discrimination of each class from the others is the main contribution of the CBFS which yields distinctive features. The selected features are then employed by classifiers to generate different classification results. Finally, a new approach is developed to combine these classification results to produce the final land cover map. Five different classifiers of Wishart Maximum Likelihood, Gaussian Maximum Likelihood, Support Vector Machine, Multi Layer Perceptron and Fuzzy Inference System are also used for classification. Given the CBFS results, two different Radarsat-2 and AirSAR PolSAR data were classified. Selected features led to improvement of about 5% in producer accuracies in comparison with two well-known Genetic Algorithm Feature Selection (GAFS) and Prototype Space Feature Selection (PSFS) methods. Moreover, Comparison results demonstrate that the fuzzy classifiers could improve the accuracies about 3% if they are suitably constructed and well designed. The achieved higher overall accuracy for the final classified map shows the effectiveness of the proposed approach over the other compared classification procedures.