In this paper, we examine and discuss modeling and prediction results of several exchange rates, with main focus on EUR/RSD, using a combination of wavelet transforms, neural networks and statistical time series analytical techniques. We have also designed a user friendly software prediction tool in MATLAB which implements the proposed model. The analyzed hybrid model combines the capabilities of two different wavelet transforms and neural networks that can capture hidden but crucial structure attributes embedded in the exchange rate. The financial time series is decomposed into a wavelet representation using two different resolution levels. For each of the new time series, a neural network is created, trained and used for prediction. In order to create an aggregate forecast, the individual predictions are combined with statistical features extracted from the original input. Additional to the conclusion that the increase in resolution level does not improve the prediction accuracy, the analysis of obtained results indicates that the suggested model sufficiently satisfies characteristics of a financial predictor.