Abstract: An artificial neural network (ANN) technique is used to predict tropical cyclone intensity change in the Western North Pacific basin and the efficacy is examined. The intensity change forecasts produced by the ANN model are compared to the results of a model developed using linear regression by National Hurricane Center (NHC) at 12-hour,24-hour,36-hour, 48-hour, 72-hour forecast periods. The date, location, track, intensity and the intensity change similarities is used to identify a historical analog tropical cyclone to the current tropical cyclone.Once the analog tropical cyclone are identified, the climatology and persistence variables, such as the previous 6-hour intensity change, location of the tropical cyclone center, current intensity at the time of the observation, are computed from the China Meteorological Administration (CMA) best-track dataset. The synoptic and dynamics variables, such as vertical sheer, sea surface temperature are computed from the National Centers for Environmental Prediction (NCEP) reanalysis data and the weekly sea surface temperature dataset. These variables and intensity of the tropical cyclone are combined as a training set to identify the variables that are best correlated with tropical cyclone intensity. The variables are used to train a neural network which uses BP algorithm as a learning rule to get the best forecast model.26 tropical cyclone processes in Western North Pacific for 2004-2005 are used to compare the ANN model with linear regression. The number of forecast cases at 12-hour, 24-hour, 36-hour, 48-hour, 72-hour forecast periods is 634, 582, 530, 478, 426 respectively.The preliminary results suggest that, errors of the ANN model are significantly smaller comparing to linear regression for the 12-hour, 24-hour, 36-hour, 48-hour, 72-hour forecast periods. This improvement is the result of the analog tropical cyclones selection and variables filter for a given tropical cyclone. Several case studies show that the ANN model is able to reproduce the processes of super typhoon and tropical cyclones with fast-enhancing intensity and regrowing cases.The results also show that the most important variables for tropical cyclone predicting are the center pressure, the intensity change, the center location (longitude and latitude) and the vertical wind sheer. But the contributions of the other variables also cannot be ignored. So the tropical cyclone intensity change is complex and nonlinear.