The unique, bi-pedal motion of humans has been shown to generate a characteristic micro-Doppler signature in the time-frequency domain that can be used to discriminate humans from not just other targets, but also between different activities, such as walking and running. However, the classification performance increasingly drops as the aspect angle between the target and radar approaches perpendicular, and the radial velocity component seen by the radar is minimized. In this paper, exploitation of the multi-static micro-Doppler signature formed from multi-angle observations of a radar network is proposed to improve oblique-angle classification performance. The concept of mutual information is applied to find the order of importance of features for a given classification problem, thereby enabling the selection of optimal features prior to classification. Strategies for fusing multistatic data using mutual information and model-based approaches are discussed.