Abstract. Hydrologic similarity between catchments, derived from their similarity in how they respond to precipitation input, is the basis for classification, for transferability, for generalization and also for understanding the potential impacts of environmental change. An important question in this context is, in how far can widely available hydrologic information (precipitation-temperature-streamflow) be used to create a first order grouping of hydrologically similar catchments? We utilize a heterogeneous dataset of 280 catchments located in the Eastern US to understand hydrologic similarity in a 6-dimensional signature space across a region with strong environmental gradients. Signatures are defined as hydrologic response characteristics that provide some insight into the hydrologic function of catchments. A Bayesian clustering scheme is used to separate the catchments into 9 classes, which are subsequently analyzed with respect to their hydrologic, as well as climatic and landscape attributes. Based on the empirical results we hypothesize the following: (1) Streamflow elasticity with respect to precipitation is modified by the soil characteristics of a catchment. (2) Spatial proximity is a good first indicator of hydrologic similarity because of the strong control climate exerts on catchment function, and because it varies slowly in space.