Myoelectric signal classification is one of the most difficult pattern recognition problems because large variations in surface electromyogram features usually exist. In the literature, attempts have been made to apply various pattern recognition methods to classify surface electromyography into components corresponding to the activities of different muscles, but this has not been very successful, as some muscles are bigger and more active than others. This results in dataset discrepancy during classification. Multicategory classification problems are usually solved by solving many, one-versus-rest binary classification tasks. These subtasks unsurprisingly involve unbalanced datasets. Consequently, we need a learning methodology that can take into account unbalanced datasets in addition to large variations in the distributions of patterns corresponding to different classes. Here, we attempt to address the above issues using hybrid features extracted from independent component analysis and twin support vector machine techniques.