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Classification of action potentials in multiunit recordings was based on the use of various types of features to uniquely characterize action potentials from different cells. The authors compared classification results obtained using three types of descriptive features: digitized data points, amplitude and duration (time domain) parameters, and fast Fourier transform (FFT) coefficients. Digitized data points used as descriptive features provided good classification success and required minimal computation. Time-domain features gave comparable results but required more computation. FFT coefficients were less effective than the other features. As the signal-to-noise ratio of the recordings increased, smaller differences in feature values could be discriminated.