A novel approach for gesture recognition based on motion history images is proposed in this paper for one- shot learning gesture recognition task. The challenge here is to perform satisfactory recognition operations with only one training example of each action, while no prior knowledge about actions, foreground/background segmentation, or any motion estimation and tracking are available. In the proposed scheme motion history imaging technique is applied to track the motion flow in consecutive frames. The information of motion flow is later utilized to calculate the percent change of motion flow for an action in different spatial regions of the frame. The space-time descriptor computed this way from the query video is a measure of the likeness of a gesture in a lexicon. Finally, gesture classification is performed based on correlation based and Euclidean distance based classifiers and the results are compared. Through extensive experimentations on a much diversified dataset the effectiveness of employing the proposed scheme is established.