The detection and classification of marine mammal vocalisations is an important component in noise mitigation strategies and in the tracking of animals for research purposes. These complex vocalisations span a broad range of frequencies with differences between and within species, and with temporal and geographical variations adding further complexity. Passive Acoustic Monitoring (PAM) systems can be deployed for long periods and can collect large volumes of data, becoming impractical for human operators to manually process due to the significant effort required. Many signal processing algorithms to automate this process have been produced with mixed results. Some are focused on the identification of single species while others handle a variety. No single algorithm is ideal for detecting and classifying all species concurrently, so any automated system requires a suite of these algorithms. A number of these algorithms are summarised here as part of an initial step in the construction of a PAM system incorporating real-time detection and classification.