Word-sense disambiguation (WSD) is the process of identifying the meanings of words in context. This article begins with discussing the origins of the problem in the earliest machine translation systems. Early attempts to solve the WSD problem suffered from a lack of coverage. The main approaches to tackle the problem were dictionary-based, connectionist, and statistical strategies. This article concludes with a review of evaluation strategies for WSD and possible applications of the technology. WSD is an ‘intermediate’ task in language processing: like part-of-speech tagging or syntactic analysis, it is unlikely that anyone other than linguists would be interested in its results for their own sake. ‘Final’ tasks produce results of use to those without a specific interest in language and often make use of ‘intermediate’ tasks. WSD is a long-standing and important problem in the field of language processing.