Abstract: A back propagation neural network based on enlarging error is proposed for improving the learning speed of multi layer artificial neural networks with sigmoid activation function It deals with the flat spots that play a significant role in the slow convergence of back propagation （BP） The advantages of the proposed algorithm are that it can be established easily and convergent with minimal mean square error It updates the weights of neural network effectively by enlarging the error term of each output unit, and keeps high learning rate to meet the convergence criteria quickly The experiments based on the well established benchmarks, such as 3 parity and soybean data sets, show that the algorithm is more efficacious and powerful than some of the existing algorithms such as Delta bar Delta algorithm, momentum algorithm, and Prime Offset algorithm in learning, and it is less computationally intensive and less required memory than the Levenberg Marquardt（LM） method