Passive acoustic monitoring allows the assessment of marine mammal occurrence and distribution at greater temporal and spatial scales than is now possible with traditional visual surveys. However, the large volume of acoustic data and the lengthy and laborious task of manually analyzing these data have hindered broad application of this technique. To overcome these limitations, a generalized automated detection and classification system (DCS) was developed to efficiently and accurately identify low-frequency baleen whale calls. The DCS (1) accounts for persistent narrowband and transient broadband noise, (2) characterizes temporal variation of dominant call frequencies via pitch-tracking, and (3) classifies calls based on attributes of the resulting pitch tracks using quadratic discriminant function analysis (QDFA). Automated detections of sei whale (Balaenoptera borealis) downsweep calls and North Atlantic right whale (Eubalaena glacialis) upcalls were evaluated using recordings collected in the southwestern Gulf of Maine during the spring seasons of 2006 and 2007. The accuracy of the DCS was similar to that of a human analyst: variability in differences between the DCS and an analyst was similar to that between independent analysts, and temporal variability in call rates was similar among the DCS and several analysts.