This paper proposes a novel class selection method based on the Kullback-Leibler (KL) information measure and outlines its application to optimal motion selection for bioelectric signal classification. When a user has no experience of controlling devices using bioelectric signals, for instance controlling a prosthetic hand using EMG signals, it is well known that voluntary generation of such signals might be difficult, so that the classification issue of multiple motions thus becomes problematic as the number of motions increases. An effective selection method for motions (classes) is needed for accurate classification. In the proposed method, the probability density functions (pdfs) of measured data are estimated through learning involving a multidimensional probabilistic neural network (PNN) based on the KL information theory. A partial KL information measure is then defined to evaluate the contribution of each class for classification. Effective classes can be selected by eliminating ineffective ones based on the partial KL information one by one. In the experiments performed, the proposed method was applied to motion selection with three subjects, and effective classes were selected from all motions measured in advance. The average classification rate using selected motions under the proposed method was 92.5 ± 0.9 %. These outcomes indicate that the proposed method can be used to select effective motions for accurate classification.