This work presents the development of a data mining application for gait pattern classification. The objective is to understand the differences and similarities among patterns of walking from healthy and unhealthy subjects groups. The data repository contains the spatial parameters of centers of pressure (CoP) trajectories during gait. The trajectory of each CoP is extracted from the contact points of the feet with the ground form each footprint. The data was collected with a GaitRite® Pressure Sensor Mat. The proposed method includes the standardization of data and creation of an organized repository (data warehouse) from previously collected data. Also, it includes the development of a process mining example to analytical comparison between these two different groups. A graphical analysis based on decision tree provides the interpretation of the pattern of ‘signature’ footprints. This study is the starting point to classifying different pathologies and assisting the rehabilitation treatments.