In this study, artificial neural network (ANN) model and the multiple linear regression (MLR) model are used to predict the numbers of tropical cyclones (TCs) forming over the western North Pacific from June to October. The correlations between the frequency of TCs and the large-scale environmental variables during boreal spring (March-May) were analyzed for a period of approximately six decades 1950-2009; subsequently eight highly correlated predictors were selected to predict the TC frequency from 2010 to 2017. A comparison between ANN and MLR models shows that ANN model exhibits better performance as compared to MLR model. Specifically, the correlation coefficient (R) reached 0.99 and the mean absolute error (MAE) was 0.77 during the historical data simulation. During the prediction period, R values of ANN and MLR models were 0.80 and 0.46, respectively. MAE values of ANN and MLR models were1.97 and 3.30, respectively, which further confirms that ANN model significantly outperforms MLR model in both simulation and prediction and has potential for application in operational forecast.