The Refiner series of intelligent systems are used to create non-overlapping category descriptions from a set of cases which have been assigned to categories by an expert.  The systems generalise values for descriptors (fields) for each of the categories to create category descriptions, and suggest to the user various means by which any inconsistencies can be removed. In this thesis we define a new expert system, AKT-R4, which is the fourth iteration of the Refiner series.  Unlike the previous Refiner systems, the focus on AKT-R4 is classification rather than the creation of unambiguous category descriptions. The AKT-R4 system is based on a case-based algorithm focussed on performing a classification task, in particular medical (differential) diagnosis (a classic classification task); AKT-R4 is aimed primarily at medical students and junior doctors (i.e. novice diagnosticians) although it is not domain specific. The AKT-R4 algorithm incorporates concepts from case-based reasoning, hypothetico-deductive reasoning and illness scripts, and performs diagnosis by means of a new concept known as the diagnosis web, which is an expansion of the illness script concept.  This system supports the novice user as he/she develops and navigates through a medical knowledge web and requires no additional knowledge acquisition beyond the specification of a set of cases which the system uses to build a model of the domain. Medical diagnosis is difficult to perform and difficult to teach as it requires a large volume of information of various kinds, and an appreciation of the differential diagnosis process.  As such, an additional benefit of the AKT-R4 system is that it can be used to tutor diagnosis. A preliminary evaluation of the system has been performed and some useful feedback has been received.