A method for automatic generation of feature recognition rules that is applicable in different application domains is proposed. This is a new method for creating knowledge bases of AFR systems that elevates the knowledge acquisition issues associated with the development of rules-based AFR systems. The two most important characteristics of this method are: 1) The application of inductive learning techniques for identification of hidden patterns in sets of feature examples. 2) The utilisation of two representation schemes that code feature information at different levels of abstraction to complement and extend the learning capabilities of the method. This research also suggests that the utilisation of inductive learning techniques for AFR has several advantages: 1) It provides a formal mechanism for rule definition and also assures the consistency of the generated rule sets. 2) The development of AFR systems for different application domains requires only representative training sets to be formed for each of them. This is a major advantage of the proposed approach due to the domain-dependent nature of features. 3) Due to the generalisation capabilities of inductive learning techniques, AFR systems could recognise features that are not present in the training sets. 4) The knowledge base of such systems could be extended easily to cover new or user-defined features.