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Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multi-degree-of-freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameter for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least-squares algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher-order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single-site MES on the basis of two features, the first time series parameter and the signal power.