A new method for quantitative analysis of interjoint coordination at various walking speeds is presented. The model imposed a parametric relationship among lower limb joint motions (hips and knees) using the least number of parameters. An integration of different analysis tools such as harmonic analysis, principal component analysis, and artificial neural networks helped overcome high-dimensionality, temporal dependence, and nonlinear relationships of the gait patterns. The trained model was fed only two control parameters (cadence and stride length) for each gait cycle and predicted the corresponding gait waveforms. Based on the differences between predicted and actual gait waveforms, a coordination score, which ranged from 0 to 10, was defined at various walking speeds. The model was applied to eight patients with knee arthroplasty at different follow-ups as well as to eight healthy subjects, walking at three different speeds. The results showed that knee replacement and rehabilitation programs improved the coordination score. The technique provides an analytical tool that can be used as a routine test in the clinical evaluation of human gait abnormalities