This paper describes preliminary work that aims to apply some learning strategies to a medical follow-up study. An investigation of the application of three machine learning algorithms1R, FOIL and InductH to identify risk factors that govern the colposuspension cure rate has been made. The goal of this study is to induce a generalised description or explanation of the classification attribute, colposuspension cure rate (completely cured, improved, unchanged and worse) from the 767 examples in the questionnaires. We looked for a set of rules that described which risk factors result in differences of cure rate. The results were encouraging, and indicate that machine learning can play a useful role in large scale medical problem solving. 1 Introduction One of the central problems of the information age is dealing with the enormous amount of raw information that is available. More and more data is being collected and stored in databases or spreadsheets. As the volume incre..