Commercially available autonomous recorders for monitoring vocal wildlife populations such as birds and frogs now make it possible to collect thousands of hours of audio data in a field season. Given limited resources, it is not practical to manually review this volume of data “by ear”. The automatic processing of sound recordings to detect and identify specific species from their vocalizations, even if not perfectly accurate, makes efficient use of researchers who review only those samples most likely to contain vocalizations of interest. This results in significant gains of sample coverage, operating efficiency, and cost savings. Developing generalized computer algorithms capable of accurate species identification in real-world field conditions is full of difficult challenges. First, recordings made by autonomous recorders typically receive sounds scattered and reflected by trees, obscured by an unpredictable constellation of random noise, wind, rustling leaves, traffic, and other species. Second, the vocalizations of many species are highly varied from one individual to the next. Any algorithm must be prepared to accept vocalizations that are similar, but not identical, to known references in order to successfully detect the previously unobserved individual. However, in so doing, the algorithm is then susceptible to misclassifying a vocalization from a different species with similar components. This is especially true for species with narrowband vocalizations lacking distinctive spectral properties and in species with short duration vocalizations lacking distinctive temporal properties. We discuss an algorithm based on Hidden Markov Models automatically constructed so as to consider not just the spectral and temporal features of individual syllables, but also how syllables are organized into more complex songs. Additionally, several techniques are employed to reduce the effects of noise present in recordings made by autonomous recorders.