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Approximate string matching is an important operation in information systems because an input string is often an inexact match to the strings already stored. Commonly known accurate methods are computationally expensive as they compare the input string to every entry in the stored dictionary. This paper describes a two‐stage process. The first uses a very compact n‐gram table to preselect sets of roughly similar strings. The second stage compares these with the input string using an accurate method to give an accurately matched set of strings. A new similarity measure based on the Levenshtein metric is defined for this comparison. The resulting method is both computationally fast and storage‐efficient.

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