Abstract: Artificial neural network(NN) as a powerful nonlinear data processing method, has been successfully applied to process electron temperature for Thomson scattering system on HL-2A. A type of perception is chosen. The NN has three layers: input layer, hidden layer, and output layer. Calibration data or measured data are the input layer, hidden layer uses sigmoid function as transfer function, and output layer is electron temperature. The calculation results fit well with that results calculated by traditional minimization chi-square method. And its calculation speed, about 1 ms per shot and per spatial point, is about 20 times faster than the minimization chi-square method. Therefore, it is possible for real time feed-back control plasma discharge by electron temperature measured by Thomson scattering on HL-2M, ITER or CFTER.