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The definition of gait events and phases have been well established in the literature through the use of qualitative movement descriptors. The repeatable, objective definitions of gait events and phases is the cornersone of sucess when performin a multi-center trial. A correlation-based multi-feature automated segmentation algorithm was developed and applied to treadmill running data. The features used were soley from 3D kinematic marker trajectory data, including generated features such as vectors between kinematic markers. The algorithm was compared against a trained tester who used visual inspection and threshold limits of the vGRF to segment stance. The automated segmentation approach was shown to consistently identify the same gait events as the trained tester, representing a significant time savings for the signal processing of large volume treadmill running data.