Pathology ordering by General Practitioners (GPs) is a significant contributor to rising health care costs both in Australia and worldwide. A thorough understanding of the nature and patterns of pathology utilization is an essential requirement for addressing the issues of appropriate pathology orderings and possible over-utilization. This paper describes how Kohonen?s Self-Organizing Maps are used to discover the most typical patterns in pathology orderings for different patient groups. Test ordering data from a pathology company in Australia is analyzed; homogenous clusters of patients with similar ordering patterns are discovered and investigated, and possible management implications are discussed. The novelty of this approach is in using data mining techniques on pathology ordering data in order to provide a patient oriented perspective for understanding the nature of ordering patterns.