Little brown bats, Myotis lucifugus, are known for their ability to echolocate and utilize their echolocation system to navigate, and locate and identify prey. Their echolocation signals have been characterized in detail but their communication signals are less well understood despite their widespread use during social interactions. The goal of this study was to develop an automatic classification algorithm for characterizing the communication signals of little brown bats. Sound recordings were made overnight on five individual male bats (housed separately from a large group of captive bats) for 7 nights, using a bat detector and a digital recorder. The spectral and temporal characteristics of recorded sounds were first analyzed and classified by visual observation of a call's temporal pattern and spectral composition. Sounds were later classified using an automatic classification scheme based on multivariate statistical parameters in MATLAB. Human- and machine-based analysis revealed five discrete classes of bat's communication signals: downward frequency-modulated calls, steep frequency-modulated calls, constant frequency calls, broadband noise bursts, and broadband click trains.