In this paper, we propose a Six Channel Analysis System (SCAnS) for the (semi-)automatic investigation of potential deception across all communication channels. SCAnS builds on our current system: Six Channel Analysis in Realtime (SCAnR) training within our BAII programme. SCAnR users are trained to code – as Points of Interest (PIns) – relevant occurrences of twenty-seven criteria relating to the six channels, when they appear to point to inconsistencies with respect to the speaker’s account (the story they are trying to convey), their apparent baseline and the context. Our experiences to date confirm the view that multi-channel approaches have the potential to lead to higher accuracy rates of deception detection than is possible when using individual methods of detection and/or when focussing on one communication channel independently (Vrij et al., 2000: 257), especially when combined with cognitive elicitation strategies. However, we recognise the importance of (in)validating the relevance of the twenty-seven criteria through ongoing research. SCAnS will provide a (semi-)automated means of achieving this. Given our audience, we focus on the usefulness of content-analysis tools like Wmatrix (Rayson, 2008) for this purpose. More broadly, our research has implications for the analysis of data in forensic contexts, across all available channels of communication, and for the coding of (para)linguistic features.