Classification of dolphin whistles has generally been a manual process requiring a trained observer, and calling for subjective and qualitative decisions. As a result it has been difficult to determine the differences and similarities between dolphin whistles. Software developed in the Underwater Acoustics Group at Loughborough University automates many of the aspects of classification of dolphin whistles, and provided quantitative probabilities for class memberships. Using the software, whistles may be detected on recordings, enhanced and background noise removed, their frequency-time contour traced and then encoded into a more compact data structure. Pattern recognition techniques can then more easily be applied to such encoded whistles. Examples of the use of the extraction, encoding, and classification methods are shown using the software, demonstrating the current levels of automation. Quantitative class probabilities for the signals are calculated which can be used to determine identities of different groups of dolphins in the field.