Human computer interaction based on hand gesture is one of the most popular natural interactive modes, which severely depends on the methods for real-time gesture recognition. Here, an effective hand feature extraction method is described, and the corresponding hand gesture recognition method is proposed. First, based on a simple tortoise model, one segments the human hand images by skin color features and tags on the wrist, and normalizes them to create the train set. Then feature vectors are computed by drawing concentric circles according to the center of the palm, and linear discriminant analysis (LDA) algorithm is used to deal with those vectors. Finally, an improved K-nearest neighbor (KNN) algorithm is presented to achieve real-time hand gesture classification and recognition. Experimental results with a self-defined hand gesture data set and multi-projector display systems prove the efficiency of the new approach.