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Recent studies on the myoelectric control of powered prosthetics revealed several factors that affect its clinical performance. One of the important factors is the variation in the limb position associated with normal use which can have a substantial impact on the robustness of Electromyogram (EMG) pattern recognition. To solve this problem, we propose in this paper a new feature extraction algorithm based on set of spectral moments that extracts the relevant information about the EMG power spectrum in an accurate and efficient manner. The main goal is to rely on effective knowledge discovery and pattern recognition methods to discover the neural information embedded in the EMG signals regardless of the limb position. Specifically, the proposed features define descriptive qualities for the general time domain-based characterization of the EMG spectral amplitude, spectral sparsity, and irregularity factor by the …