A great number of associative memory models have been proposed to realize information storage and retrieval inspired by human brain in the last few years. However, there is still much room for improvement for those models. In this paper, we extend a binary pattern associative memory model to accomplish real-world image recognition. The learning process is based on the fundamental Hebb rules and the retrieval is implemented by a normalized dot product operation. Our proposed model can not only fulfill rapid memory storage and retrieval for visual information but also have the ability on incremental learning without destroying the previous learned information. Experimental results demonstrate that our model outperforms the existing Self-Organizing Incremental Neural Network (SOINN) and Back Propagation Neuron Network (BPNN) on recognition accuracy and time efficiency.