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In this paper, a novel object classification method is introduced and developed within a biomechanical study of human knee function in which subjects are classified to one of two groups: subjects with osteoarthritic (OA) and normal (NL) knee function. Knee-function characteristics are collected using a three-dimensional motion-analysis technique. The classification method transforms these characteristics into sets of three belief values: a level of belief that a subject has OA knee function, a level of belief that a subject has NL knee function, and an associated level of uncertainty. The evidence from each characteristic is then combined into a final set of belief values, which is used to classify subjects. The final belief values are subsequently represented on a simplex plot, which enables the classification of a subject to be represented visually. The control parameters, which are intrinsic to the classification method, can be chosen by an expert or by an optimization approach. Using a leave-one-out cross-validation approach, the classification accuracy of the proposed method is shown to compare favorably with that of a well-established classifier-linear discriminant analysis. Overall, this study introduces a visual tool that can be used to support orthopaedic surgeons when making clinical decisions.