Techniques for automatic recognition of isolated words are investigated, and a computer simulation of a word recognition system is effected. Considered in detail are data acquisition and digitizing, word detection, amplitude and time normalization, short-time spectral estimation including spectral windowing, spectral envelope approximation, parametric and adaptive feature extraction, and pattern classification. Recognition parameters are derived from estimated short-time power spectra. An evaluation of spectral windows is made resulting in the selection of the hanning window. A convolutional smoothing method of spectral envelope approximation is developed, and several smoothing windows are compared. Features are extracted from partitioned intervals over the spectral envelopes. The boundaries of these intervals are determined using both parametric and adaptive procedures. An improved measure of feature merit is developed and is compared with both the information and analysis-of-variance criteria. The overall word recognition system was found to perform very satisfactorily with recognition accuracies reaching 99 percent for a single speaker, 50-word vocabulary and 96 percent for a three-speaker, 30-word vocabulary. (Author/GO)