© 2015 Taylor & Francis. Sophisticated machine learning algorithms have been successfully applied to functional neuroimaging data in order to characterize internal cognitive states. But is it possible to “mind-read” without the scanner? Capitalizing on the robust finding that the contents of working memory guide visual attention toward memory-matching objects, we trained a multivariate pattern classifier on behavioural indices of attentional guidance. Working memory representations were successfully decoded from behaviour alone, both within and between individuals. The current study provides a proof-of-concept for applying machine learning techniques to simple behavioural outputs (e.g., response times) in order to decode information about specific internal cognitive states.