Recognizing human activities from video sequences or sensor data is a challenging task in computer vision. Background clutter, partial occlusion, changes in viewpoint, lighting, and appearance are creating bottlenecks in the recognition of activity. In this paper, we provide a comprehensive review by categorizing the activity recognition approaches that have been applied on multivariate time series data. The review provides insights of each method, research issues and performance issue.