Knowledge discovery from the dramatically increased data of an auto-stored medical information system is still in its infancy. The purpose of this study is to use widely available and easily operated techniques that can satisfy general users in extracting specific knowledge to make the medical information system more functional. Data mining techniques, including data visualisation, correlation analysis, discriminant analysis, and neural networks supervised classification, were applied to heart disease databases. These techniques can help to identify high risk patients, define the most important factors (variables) in heart disease, and build a multivariate relationship model to show the relationship between any two variables in a way that such relationships are easy to view. Simple visualization techniques were utilised to construct this model, which corresponds with current medical knowledge. Two nonparametric (distribution assumption free) classification tools were employed to identify high risk heart disease patients. Both the neural networks supervised classification methods and the discriminant analysis method produced reliable classification rates for heart disease patients. However, neural networks yielded a higher percentage of correct classifications (averaging 89%) than discriminant analysis (79%). Data visualisation and correlation analysis resulted in similar conclusions regarding the most important factors in heart disease. These data mining tools provide simple and effective methods of extracting knowledge from general medical information. The treatment of missing data is also discussed.