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Across much of the world, wind gust data are continuously measured by Automatic Weather Stations (AWS). However, the meteorological origin of individual extreme gust events within these datasets are seldom automatically assigned. To overcome this, Self-Organizing Maps (SOM) are proposed here as an automated tool to classify gust events within 1-min AWS data. In this paper, this method is specifically used to distinguish between wind gust events of convective (often broadly termed non-synoptic or thunderstorm) and non-convective origin, with the latter events further sub-classified as either, wind only, transition, or other. The efficacy of a range of different SOMs and input variables were assessed, and it was found that those that utilised gust wind speed, temperature, and pressure generally outperformed other models when classifying wind gusts. Applying this approach in the Australian context, all wind gusts of convective origin greater than 70 ​km ​h−1 (19.4 ​m ​s−1) and 90 ​km ​h−1 (25 ​m ​s−1) were identified at 306 AWS across Australia and a climatology of seasonal and annual convective wind gust occurrence developed and discussed. • Gusts from 1-min weather station data are categorised using machine-learning. • Self-Organizing Maps are shown to categorise storm modes accurately and efficiently. • Convective gusts are best classified using wind speed, pressure and temperature data. • Convective gust climatology developed for Australia. [ABSTRACT FROM AUTHOR]