Forecasts of food prices are intended to be useful for farmers, policymakers and agribusiness industries. In the present era of globalization, management of food security in the agriculture-dominated developing countries like India needs efficient and reliable food price forecasting models more than ever. Sparse and time lag in the data availability in developing economies, however, generally necessitate reliance on time series forecasting models. The recent innovation in Artificial Neural Network (ANN) modelling methodology provides a potential price forecasting technique that is feasible given the availability of data in developing economies. In this study, the superiority of ANN over linear model methodology has been demonstrated using monthly wholesale price series of soybean and rapeseed-mustard. The empirical analysis has indicated that ANN models are able to capture a significant number of directions of monthly price change as compared to the linear models. It has also been observed that combining linear and nonlinear models leads to more accurate forecasts than the performances of these models independently, where the data show a nonlinear pattern. The present study has aimed at developing a user-friendly ANN based decision support system by integrating linear and nonlinear forecasting methodologies.