A new method for extracting feature sets with improved reconstruction and classification performance in computer vision applications is presented in this paper. The main idea is to propose a procedure for obtaining surrogates of the compressed versions of very reliable feature sets without affecting significantly their reconstruction and recognition properties. The surrogate feature vector is of lower dimensionality and thus more appropriate for pattern recognition tasks. The proposed feature extraction method (FEM) combines the advantages of the multiresolution analysis, which is based on the wavelet theory, with the high discriminative nature of Zernike moment sets. The resulted feature vector is used as a classification feature, in order to achieve high recognition rates in a typical pattern recognition system. The results of the experimental study support the validity and the strength of the proposed method.