When a serial offender strikes, it usually means that the investigation is unprecedented for that police agency. The volume of incoming leads and pieces of information in the case(s) can be overwhelming as evidenced by the thousands of leads gathered in the Ted Bundy Murders, Atlanta Child Murders, and the Green River Murders. Serial cases can be long term investigations in which the suspect remains unknown and continues to perpetrate crimes. With state and local murder investigative systems beginning to crop up, it will become important to manage that information in a timely and efficient way by developing computer programs to assist in that task. One vital function will be to compare violent crime cases from different jurisdictions so investigators can approach the investigation knowing that similar cases exist. CATCH (Computer Aided Tracking and Characterization of Homicides) is being developed to assist crime investigations by assessing likely characteristics of unknown offenders, by relating a specific crime case to other cases, and by providing a tool for clustering similar cases that may be attributed to the same offenders. CATCH is a collection of tools that assist the crime analyst in the investigation process by providing advanced data mining and visualization capabilities.These tools include clustering maps, query tools, geographic maps, timelines, etc. Each tool is designed to give the crime analyst a different view of the case data. The clustering tools in CATCH are based on artificial neural networks (ANNs). The ANNs learn to cluster similar cases from approximately 5000 murders and 3000 sexual assaults residing in a database. The clustering algorithm is applied to parameters describing modus operandi (MO), signature characteristics of the offenders, and other parameters describing the victim and offender. The proximity of cases within a two-dimensional representation of the clusters allows the analyst to identify similar or serial murders and sexual assaults.