The modelling of the Automatic Target Detection, Recognition and Identification performance in systems of multiple sensors and/or platforms is important in many respects. For example, in the selection of sensors or sensor combinations of sufficient effectiveness to achieve operational requirements, or for understanding how the system might be best exploited. It is possible that a sensing system may be comprised of sensors of several different types, including active and passive approaches in the radio frequency and optical portions of the spectrum. Some may have well-understood performance, whereas others may be only poorly characterised. A simulation framework has been developed examining sensor options across different sensor types, parameterisations, search strategies, and applications. The framework is based around Bayesian Decision Theoretic principles along with simple sensor models and search environment. It uses Monte-Carlo simulation to derive statistical measures of performance for systems. The framework has been designed to encompass detection, recognition and identification problems and also to treat sensor characterisation. The modelling framework has been applied to a number of illustrative problems. These range from simple target detection scenarios using sensors of differing performance or of different regional search schemes, through to examinations of: the number of measurements required to reach threshold performance; the effects of sensor measurement cost; issues relating to the poor characterisation of sensors within the system, and; the performance of combined detection and recognition sensor systems. Results are presented illustrating these effects. These generally show that the method is able to quantify qualitative expectations of performance, and is sufficiently powerful to highlight some unexpected aspects of operation.