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Surface electromyogram (sEMG) is a measure of the muscle activity from the skin surface, and is an excellent indicator of the strength of muscle contraction. It is an obvious choice for control of prostheses and identification of body gestures. Using sEMG to identify posture and actions that are a result of overlapping multiple active muscles is rendered difficult by interference between different muscle activities. In the literature, attempts have been made to apply independent component analysis to separate sEMG into components corresponding to the activities of different muscles, but this has not been very successful, because some muscles are larger and more active than the others. We address the problem of how to learn to separate each gesture or activity from all others. Multicategory classification problems are usually solved by solving many one-versus-rest binary classification tasks. These subtasks naturally involve unbalanced datasets. Therefore, we require a learning methodology that can take into account unbalanced datasets, as well as large variations in the distributions of patterns corresponding to different classes. This paper reports the use of twin support vector machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications.