It is becoming increasingly apparent that without some form of explanation capability, the full potential of trained Artificial Neural Networks (ANNs) may not be realised. This seminar gives an overview of techniques developed to redress this situation. Specifically the seminar focuses on mechanisms, procedures, and algorithms designed to insert knowledge into ANNs (knowledge initialisation), extract rules from trained ANNs (rule extraction), and utilise ANNs to refine existing rule bases (rule refinement). The seminar also introduces a taxonomy for classifying the various techniques, discusses their modus operandi, and delineates criteria for evaluating their efficacy. Finally, results from data mining and natural language processing experiments will be presented.