Rules are commonly used for classification because they are modular, intelligible and easy to learn. Existing work in classification rule learning assumes the goal is to produce categorical classifications to maximize classification accuracy. Recent work in machine learning has pointed out the limitations of classification accuracy: when class distributions are skewed, or error costs are unequal, an accuracy maximizing classifier can perform poorly. This paper presents a method for learning rules directly from ROC space when the goal is to maximize the area under the ROC curve (AUC). Basic principles from rule learning and computational geometry are used to focus the search for promising rule combinations. The result is a system that can learn intelligible rulelists with good ROC performance.