Case-based reasoning (CBR) systems rely on the conceptual ordering of entities called cases. If atomic case features are allowed to assume numeric as well as symbolic values, then a systematic comparison regime is needed to aggregate similarity scores. A common approach to deal with real-numbered features is normalisation. However, there are two conspicuous problems with this procedure: (1) The similarity between two features is dependent on the corresponding values of all other cases to be ranked. (2) Real-numbered features are often interpreted by human experts according to conceptual constraints associated with features. In such situations, a conceptual distance between two features should be determined, rather than the length of a ?gap? on a linear scale. Within the framework of a comprehensive case-knowledge architecture, the notion of a concept frame that can be associated with a case feature is proposed. Through this component it is possible to represent polymorphic atomic case features, and to systematically establish the concept distance between two real-numbered feature instances.