Kinematic parameters for 10 normal subjects and 10 patients with ankle arthrodesis are grouped using the fuzzy cluster paradigm. The features chosen for clustering are Euler angles of the sagittal plane in the hindfoot, the forefoot and combined hindfoot and forefoot joints. Gait patterns are identified using information provided by cluster validity techniques, giving three, three and two clusters for the hindfoot, forefoot and combined hindfoot and forefoot joints, respectively. The cluster centers represent distinct walking strategies adopted by normal subjects and patients after ankle arthrodesis. Utilizing angle values normalized by gait cycle, it is possible to classify any subject and to generate an individual's membership value for each of the clusters. The clinical utility of the fuzzy clustering approach is demonstrated with data for subjects with ankle arthrodesis, where changes in membership of the clusters provide an objective technique for measuring changes of gait pattern after ankle arthrodesis. This approach can be adopted to study other clinical entities where different cluster centers would be established using the algorithm provided in this study.