This article provides a complete description of the subprofile scaling model (SSM) approach to the analysis of positron emission tomography (PET) data. The goals and assumptions underlying the development of SSM are outlined, and its strengths and weaknesses are discussed. It is demonstrated that all obtainable information about regional ratios can, in theory, be derived from the SSM regional covariance patterns. A general constraint on the ability to effectively remove global variation while identifying region-specific information about PET data sets is outlined and discussed within the SSM framework. Finally, an extension of the SSM technique to the generation of disease-specific covariance patterns is demonstrated for paraneoplastic cerebellar degeneration, the acquired immune deficiency syndrome dementia complex, and Parkinson's disease, and the importance of multidimensional descriptions of disease, such as may be obtained from PET data using SSM, is emphasized.