This study aims to experimentally investigate the feasibility of discriminating human motions with the help of micro Doppler features by using radar. In the first phase, different time-frequency transformations are applied on the simulated walking data and the results of the simulator are compared with field experiments. In the following phase, several field experiments which are in the scope of the simulator are conducted and the experimental data for running, crawling, creeping and walking with the aspect angles of 0°, 30°, 60° are collected by using a ground surveillance radar. Signal processing steps and micro Doppler processing steps are applied to the collected data and spectrograms are obtained. Six features, which are torso frequency, bandwidth of the signal, offset of the signal, bandwidth without micro Dopplers, the standard deviation of the signal strength, the period of the arms or legs motions are extracted from the spectrograms and the efficiency of the features in motion classification is compared. Lastly, a simple neural network based classifier is constructed. The classification performances of different human motions by neural network classification are examined.