Abstract: The goal of this research was to investigate neural network-based methods to be applied in the processing of biomedical signals. We developed a neural network-based method for the detection of voluntarily produced changes in facial muscle action potentials. Electromyographic signals were recorded from the corrugator supercilii and zygomaticus major facial muscles. The facial muscle action potentials of thirty subjects were measured while they performed a series of voluntary contractions of these muscles. Wavelet denoising or digital bandpass filtering was applied to the preprocessing of the signals. A neural network was exploited for an offline classification of various phases of these signals. The results show that the neural network-based technique developed functioned very well, producing a reliable recognition accuracy of 96 to 99%. Because of these promising results, we will proceed in the development of this method for real-time applications that benefit from the analysis of electromyographic signals.