Generating both accurate as well as explanatory classification rules is becoming increasingly important in a knowledge discovery context. In this paper, we investigate the power and usefulness of fuzzy classification rules for data mining purposes. We propose two evolutionary fuzzy rule learners: an evolution strategy that generates approximate fuzzy rules, whereby each rule has its own specific definition of membership functions, and a genetic algorithm that extracts descriptive fuzzy rules, where all fuzzy rules share a common, linguistically interpretable definition of membership functions in disjunctive normal form. The performance of the evolutionary fuzzy rule learners is compared with that of Nefclass, a neurofuzzy classifier, and a selection of other well-known classification algorithms on a number of publicly available data sets and two real life Benelux financial credit scoring data sets. It is shown that the genetic fuzzy classifiers compare favourably with the other classifiers in terms of classification accuracy. Furthermore, the approximate and descriptive fuzzy rules yield about the same classification accuracy across the different data sets.