Forensic inferential reasoning is a 'fact-finding' journey for crime investigation and evidence presentation. In complex legal practices involving various forms of evidence, conventional decision making processes based on human intuition and piece-to-piece evidence explanation often fail to reconstruct meaningful and convincing legal hypothesis. It is necessary to develop logical system for evidence management and relationship evaluations. In this paper, a forensic application-oriented inferential reasoning model has been devised base on Bayesian Networks. It provides an effective approach to identify and evaluate possible relationships among different evidence. The model has been developed into an adaptive framework than can be further extended to support information visualisation and interaction. Based on the system experiments, the model has been successfully used in verifying the logical relationships between DNA testing results and confessions acquired from the suspect in a simulated criminal investigation, which provided a firm foundation for the future developments.