Kohonen's Self Organizing Feature Maps (SOFM) and other unsupervised clustering methods generate groups based on the identification of various discriminating features. These methods seek an organization in the dataset and form relational organized clusters. However, these clusters may or may not have any physical analogues. A calibration method that relates SOM clusters to physical reality, is desirable. This calibration method must define the relationship between the clusters and the observed physical properties, it should also provide an estimate of the validity of the relationships. With the development of a calibrated relationship, the whole dataset can be classified. The principal steps, therefore, are the Three-C's "Clustering, Calibration and Classification".