The purpose of this project was to develop software prototypes that demonstrate algorithms for analyzing and comparing large databases of crime data. The scope of the project was limited to specifically show how artificial neural network technology can support a multivariate or information fusion approach to finding significant patterns in crime data. The project developed CATCH (Computer Aided Tracking and Characterization of Homicides) and CATCHRAPE, which analyze homicide and sexual assault data, respectively. The development of CATCH was made possible with the HITS (Homicide Investigation Tracking System) database system. The database contains several thousand violent crimes, primarily from the Pacific Northwest. The CATCH software assesses likely characteristics of unknown offenders, relates a specific crime case to other cases, and provides a tool for clustering similar cases that may be attributed to the same offenders. The three main concepts in CATCH that provide value to investigators are the clustering algorithm, database mining, and database visualization. The report includes a CATCH software manual. Figures, notes, references, bibliographies, appendix