In this paper we present a novel methodology for textual case-based reasoning. This technique is unique in that it automatically discovers case and similarity knowledge, is language independent, is scaleable and facilitates semantic similarity between cases to be carried out inherently without the need for domain knowledge. In addition it provides an insight into the thematical content of the case-base as a whole, which enables users to better structure queries. We present an analysis of the competency of the system by assessing the quality of the similarity knowledge discovered and show how it is ideally suited to case-based retrieval (querying by example).