NOEMON: Design, implementation and performance results of an intelligent assistant for classifier selection. he selection of an appropriate classification model and algorithm is crucial for effective knowledge discovery on a dataset. For large databases, common in data mining, such a selection is necessary, because the cost of invoking all alternative classifiers is prohibitive. This selection task is impeded by two factors. First, there are many performance criteria, and the behaviour of a classifier varies considerably with them. Second, a classifier’s performance is strongly affected by the characteristics of the dataset. Classifier selection implies mastering a lot of background information on the dataset, the models and the algorithms in question. An intelligent assistant can reduce this effort by inducing helpful suggestions from background information. In this study, we present such an assistant, NOEMON. For each registered classifier, NOEMON measures its performance for a collection of datasets. Rules are induced from those measurements and accommodated in a knowledge base. The suggestion on the most appropriate classifier(s) for a dataset is then based on those rules. Results on the performance of an initial prototype are also given.