Prediction of rainfall has always been one of the most important issues in the hydrological cycle and it is essential in water resource development, planning and management of flood and drought. With the development of Artificial Intelligence (AI), a number of AI methods such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System, Fuzzy Logic, Support Vector Machine and Evolutionary Optimization Algorithm are widely applied for rainfall prediction. The ANN represents a complex non-linear relationship and extract the dependence between variables through the training process and hence used for rainfall prediction. In this paper, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network models are used for training the network data. The performance of MLP and RBF models used in rainfall prediction have been evaluated by Model Performance Indicators (MPIs) viz., Correlation Coefficient (CC), Mean Absolute Error (MAE) and Nash–Sutcliffe model Efficiency coefficient (NSE). The study presents that the MAE in rainfall prediction using MLP network is minimum than the value of RBF for Pune and Mahabaleshwar. The study also presents that the CC values vary from 0.967 to 0.978 for Pune while 0.970 to 0.987 for Mahabaleshwar. During testing period, the NSE in rainfall prediction using MLP and RBF networks for Pune is computed as 93.8% and 94.3% respectively. For Mahabaleshwar, the NSE values obtained from MLP and RBF networks are computed as 97.2% and 92.8% respectively. Based on the results of MPIs, the study suggests that the MLP is better suited amongst two networks adopted for prediction of rainfall for Pune and Mahabaleshwar.