In many classiffication problems the domains of the attributes and the classes are linearly orderded. For such problems the classiffication rule often needs to be order-preserving or monotone as we call it. Since the known decision tree methods generate non-monotone trees, these methods are not suitable for monotone classiffication problems. We provide an order-preserving tree-generation algorithm for multi-attribute classiffication problems with k linearly ordered classes, and an algorithm for repairing non-monotone decision trees. The performance of these algorithms is tested on random monotone datasets.