To address knowledge acquisition issues associated with the development of AFR systems, a new method for generating automatically feature recognition rules is proposed. In particular, these rules are formed by applying an inductive learning algorithm on training data consisting of feature examples. The creation of training data is comprised of three steps. First, a taxonomy that represents the feature classes for a given application domain is defined. Second, a set of B-Rep models representing examples of features is designed for each class of a given taxonomy. Third, the B-Rep models of features are converted into data files that are suitable for inductive learning. In particular, two levels of abstraction are considered for coding topological and geometrical information from the B-Rep feature models. The algorithm utilised in this research is RULES-5. During the induction process, RULES-5 repeatedly takes an example not covered by the previously created rules and forms a new rule for it until all examples in the training data are covered by the generated rule set.