This paper presents the development of a computerised, non-invasive psychological profiling system, ‘Silent Talker’, for the analysis of non-verbal behaviour. Nonverbal signals hold rich information about mental, behavioural and/or physical states. Previous attempts to extract individual signals and to classify an overall behaviour have been time-consuming, costly, biased, error-prone and complex. Silent Talker overcomes these problems by the use of Artificial Neural Networks. The testing and validation of the system was undertaken by detecting processes associated with ‘deception’ and ‘truth’. In a simulated theft scenario thirty-nine participants ‘stole’ (or didn't) money, and were interviewed about its location. Silent Talker was able to detect different behaviour patterns indicative of ‘deception’ and ‘truth’ significantly above chance. For example, when 15 European men had no prior knowledge of the exact questions, 74% of individual responses ( p < 0.001) and 80% ( p = 0.035) of interviews were classified correctly. Copyright © 2006 John Wiley & Sons, Ltd.