This work concentrates on recognizing persons according to their walk dynamics (gait). The primary objective is to design and implement a new method for measuring similarity of gait features that are extracted from trajectories of 3D coordinates of body components moving in time. The proposed method introduces signals of body point pairs' distance-time dependency while walking as a basic unit of gait pattern. The signals are consequently normalized with respect to a novel distance function. Parameters of the distance function are optimized regarding the recognition rate and achieved results are experimentally evaluated. The textual part contains a comprehensive survey of existing approaches to human gait recognition, as well as a description of a suitable model of human movement represented by 3D trajectories, a method for extracting gait features, a proper similarity function for effective comparison of extracted features, and results by analyzing recognition rate.