In this paper, we proposed an effective method which can recognize dynamic hand gesture by analyzing the information of motion trajectory captured by leap motion in three-dimension space. A simple gesture spotting is tried. And the orientation characteristics are quantified and coded as the feature after pre-processing the data. Then an improved discrete HMM algorithm is utilized to model and classify gestures. Experimental results on a self-built database of dynamic hand gestures (numbers 0–9) demonstrate the effectiveness of the proposed method.