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This thesis develops and demonstrates methods of classifying ocean processes using an underwater moving platform such as an Autonomous Underwater Vehicle (AUV). The mingled spectrum principle is established which concisely relates observations from a moving platform to the frequency-wavenumber spectrum of the ocean process. For classifying different processes, an AUV is not only able to jointly utilize the time-space information, but also at a tunable proportion by adjusting its cruise speed. Based on the mingled spectrum principle, a parametric tool for designing an AUV-based spectral classifier is developed. As a case study, AUV-based classification is applied to distinguish ocean convection from internal waves. To allow for mismatch between modeled templates and real measurements, the AUV-based classifier is designed to be robust to parameter uncertainties. By simulation tests on the classifier, it is demonstrated that at a higher AUV speed, convection's distinct spatial feature is highlighted to the advantage of classification. Experimental data are used to test the AUV-based classifier. An AUV-borne flow measurement system is designed and built using, an Acoustic Doppler Velocimeter (ADV). In February 1998, the AUV acquired field data of flow velocity in the Labrador Sea Convection Experiment. The classification test result detects convection's occurrence. The thesis work provides an important foundation for future work in autonomous detection and sampling of oceanographic processes.