Robust decision support systems (DSSs) for crime investigation are dfficult to construct because of the almost infinite variation of plausible crime scenarios. Thus existing
approaches avoid any explicit reasoning about crime scenarios. They focus on problems such as intelligence analysis and
profiling. This paper introduces a novel model based reasoning technique that enables DSSs to automatically construct
representations of crime scenarios. It achieves this by storing the component events of the scenarios instead of entire
scenarios and by providing an algorithm that can instantiate and compose these component events into useful scenarios. This approach is more adaptable to unanticipated
cases than one that represents scenarios explicitly because
it allows component events to match the case under investigation in many different ways. The approach presented
herein is applied to and illustrated with examples from an
application of the differentiation between homicidal, suicidal, accidental and natural death.