Hydrologic similarity between catchments, derived from similarity in how catchments respond to precipitation input, is the basis for catchment classification, for transferability of information, for generalization of our hydrologic understanding and also for understanding the potential impacts of environmental change. An important question in this context is, how far can widely available hydrologic information (precipitation-temperature-streamflow data and generally available physical descriptors) 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 insight into the hydrologic function of catchments. A Bayesian clustering scheme is used to separate the catchments into 9 homogeneous classes, which enable us to interpret hydrologic similarity with respect to similarity in climatic and landscape attributes across this region. We finally derive several hypotheses regarding controls on individual signatures from the analysis performed here.