Increasing concern about decline in biodiversity has created a demand for population surveys. Acoustic monitoring is an efficient non-invasive method, which may be deployed for surveys of animals as diverse as insects, birds, and bats. Long-term unmanned automatic monitoring may provide unique unbiased data from a whole season, but the large amount of data presents serious challenges for the automatic processing of the measurements. To demonstrate feasibility of automatic multi-channel surveying using a new prototype hardware, we carried out a 2-month study of echolocating bats requiring high data sampling rates (500kHz). Using a sound energy threshold criterion for triggering recording, we collected 236GB (Gi=10243) of data at full bandwidth. We implemented a simple automatic method using a Support Vector Machine (SVM) classifier based on a combination of temporal and spectral analyses to classify events into bat calls and non-bat events. After experimentation we selected duration, energy, bandwidth, and entropy as classification features to identify short high energy structured sounds in the right frequency range. The spectral entropy makes use of the orderly arrangement of frequencies in bat calls to reject short noise pulses, e.g. from rain. The SVM classifier reduced our dataset to 162MB of candidate bat calls with an estimated accuracy of 96% for dry nights and 70% when it was raining. The automatic survey revealed calls from two species of bat not previously recorded in the area, as well as an unexpected abundance of social calls. The recordings provide data which can be used to correlate bat activity with rain, temperature, and sunset/sunrise. We discuss future applications, achieving higher accuracy in classifying bat calls and the possibility of using trajectory-tracking data to determine bat behavior and correct for the bias toward loud bats inherent in acoustic surveying.