Transformation of a segment of acoustic signal, by processing into a vectorial representation such as the spectrum, can permit the identification of the constituent phonemes within spoken speech. Subsequent comparison against a previously stored representation using techniques such as dynamic time warping or hidden Markov modelling then permits a speech recognition operation to be accomplished. These signal-processor-intensive transform and graph-search-based pattern-matching techniques are reviewed and currently achievable recognition accuracies are reported.