Clinical gait analysis is an area aimed at the provision of support for diagnoses and therapy considerations, the development of bio-feedback systems to train patients, and the recognition of effects of multiple diseases and still active compensation. The data recorded with ground reaction force measurement platforms is a convenient starting point for gait analysis. The authors argue in favor of using the raw data from such force platforms and apply artificial neural networks for gait malfunction identification. They discuss their latest results in this line of research by using a supervised learning rule. The employed classification approach is learning vector quantization which proved to be highly robust in the training process yielding a remarkably high recognition accuracy of gait patterns.