OBJECTIVES: It is a common practice in outcomes research studies to examine several co-morbidities over two or more cohorts to develop some intuition about the health status of each group.  It is also a common practice in claims data to record the co-morbid condition as a binary variable (i.e., absence or presence of the co-morbidity).  If an investigator is interested in studying the absence or presence of 10 co-morbidities, he or she will have to consider 210= 1024 possible co-morbidity patterns, a seemingly daunting task. The common current practice is to construct summary tables and examine them to understand cohort co-morbidity patterns.  Even with this summarization, it is difficult to deduce what the composition is for the cohort over all co-morbidities simultaneously.  Once again, this can be a daunting task.  The objective of this research is to develop a means of summarizing these rich and somewhat complex data to enhance clinical decision making. METHODS: Graphical approaches for the summarization of data enable geometry, scaling, shading, and or color to describe such “high dimensional” data.  The author will introduce a novel means of plotting the co-morbid conditions that will afford investigators the ability to study patterns of co-morbidities simultaneously and understand the relative frequencies of their occurrence in one display. RESULTS: The use of a novel graphical procedure (a Saturn plot) allows an investigator to examine co-morbidity patterns readily when the number of binary co-morbidities is 10 without having to resort to poring over several tables or one large table partitioned into smaller ones based on co-morbidities. CONCLUSIONS: A newly developed graphical data summary called a Saturn plot allows investigators to indentify the relative frequency of various subgroups (as defined by their co-morbidity pattern) within a cohort without the need to study large sets of tables.