Under competitive electricity markets, various long-term and short-term contracts based on spot price are implemented by independent market operator (IMO). An accurate forecasting technique for spot price facilitates the market participants to develop bidding strategies in order to maximize their benefit. Neural-Wavelet is a powerful method for forecasting problems under the condition of nonlinearity as well as uncertainty. In this paper, a new methodology based upon radial basis function (RBF) network is proposed to the forecasting spot price problem. To train the network, in order to apply historical information of the price behavior, some other effective parameters are used. Load level, fuel price, generation and transmission location as well as conditions are the effective parameters which are associated with general well known parameters. All these parameters are applied for learning process to an assumed neural wavelet network (NWN). Simulation results are presented in details in this paper, where these results indicate the effectiveness of the proposed forecasting tool as an accurate technique.