In this paper, we describe an algorithm which can automatically recognize human gesture in a sequence of natural video image by utilizing two dimensional features extracted from bodily region of the images. In the algorithm, we first construct a gesture model space by analyzing the statistical information of sample images with principle component analysis method. And then, input images are compared to the model and individually symbolized to one part of the model space. In the last step, the symbolized images are recognized with HMM as one of model gestures. The feature of our method is to use a combination of partial and global information of two-dimensional abstract bodily motion, consequently it is very convenient to apply to real world situation and the recognition results are very robust.