Principles of the photorefractive perceptron learning algorithm are described. The influences of the finite response time and hologram erasure of the photorefractive gratings on the convergence property of the photorefractive perceptron learning are discussed. A novel neural network which could resolve these constraints is presented. It is a hybrid system which utilizes the photorefractive holographic gratings to implement the inner product between the input image and the interconnection matrix. A personal computer is used for storing the interconnection matrix and the updating procedure, and it also functions as a feedback means during the learning phase. After training the weight vectors are recorded in the volume hologram of an optical processor. This novel method combines the advantages of the massive parallelism of optical systems and the programmability of electronic computers. Experimental results of image classification are presented. It shows that the system could correctly classify the input patterns into one of the two groups after training on four examples in each group during successive iterations. The system has been extended to perform multi-category image classification.