This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis, which aims at controlling movements of patients affected by neurological diseases. The proposed approach is intended to a feature selection procedure as an optimization strategy based on genetic algorithms, where the misclassification error of healthy/diseased patients is adopted as the fitness function. This procedure will be used for estimating the performance of widely used classification algorithms, whose performance has been ascertained in many real-world problems with respect to well-known classification benchmarks, both in terms of number of selected features and classification accuracy. Moreover, the technique herein described will provide a useful tool in the context of medical diagnosis. In fact, we will prove that for the classification problem at hand the whole set of features is redundant and it can be significantly pruned. The obtained results on a real dataset acquired in our biomechanics laboratory show a very interesting classification accuracy using six features only among the sixteen acquired by the stereophotogrammetric system.