Abstract: Both traditional statistical methods and Back Propagation（BP） neural network methods have their limitations in monitoring autocorrelated industrial processes.Traditional statistical methods typically result in high false alarm rate.BP neural network methods usually lead to low sensitivity and cause difficulties in network weights training.Based on associative learning and discrete Hopfield network，a new statistical control method for autocorrelated processes is proposed.The method doesn’t need training samples.By using orthogonal coding，the process states are stored into Hopfield network in the form of attractors.Then the step shift of the autocorrelated process is detected out by using the associative phase of the Hopfield network.The case studies show that，the Average Run Levels（ARL） of the proposed method increase 27.9% and 55.0% when there is no step shift in the process and decrease at least 74.1% and 81.8% when step shift exists，respectively，compared with Elman networks and EWMA.