Satellite data have been extensively used to identify volcanic behavior. However, the physical subsurface processes causing any individual manifestation of activity can be ambiguous. We propose a classification scheme for the cause of unrest that simultaneously considers three multiparameter satellite observations. The scheme is based on characteristics of the volcanic system (open, closed, and eruptive) and unrest mechanisms (intrusion, evolution, and withdrawal) occurring at shallow depths in the volcanic system. We applied these models to satellite observations acquired at 47 of the most active volcanoes in Latin America. Of the volcanoes studied, 44 had a robust enough dataset for classification and were clustered into 4 groups and 10 subgroups with common behavioral characteristics. By identifying that these volcanoes can be clustered into a number of groupings significantly less than the number of volcanoes, we have demonstrated that commonalities in behavior patterns exist among diverse volcanic systems. Identifying volcanoes with similar characteristics underpins the use of past observations at one volcano to forecast activity at another and diverges from typical volcanic groupings, which are focused on geologic parameters (i.e., composition, volcano type, and tectonic setting). Based on satellite data alone, we have identified preeruptive intrusion prior to 15 eruptions at 12 different volcanoes, magma evolution prior to 18 eruptions at 13 volcanoes, and magma withdrawal at 3 eruptions and 3 volcanoes. Improvements to the spatial and temporal resolution are needed to make these relations robust. This classification scheme provides a framework for future automated clustering of volcanoes.