In this paper a Feature Ranking algorithm for classification is proposed, which is based on the notion of Bayes decision border. The method elaborates upon the results of the Decision Border Feature Extraction approach, exploiting properties of eigenvalues and eigenvectors of the orthogonal transformation to calculate the discriminative importance weights of the original features. Non parametric classification is also considered by resorting to Labeled Vector Quantizers neural networks trained by the BVQ algorithm. The choice of this architecture leads to a cheap implementation of the ranking algorithm we call BVQ-FR. The effectiveness of BVQ-FR is tested on real datasets. The novelty of the method is to use a feature extraction technique to assess the weight of the original features, as opposed to heuristics methods commonly used.