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This article proposes a neural network-based approach to increase accuracy of two-dimensional direction of arrival (DOA) estimation of an electromagnetic signal. The proposed method combines two neural networks developed using simulated and small amount of empirical data, respectively. The output of the simulation-based neural network represents approximate information on DOAs. It is then considered as a priori knowledge for the small empirical network that is crucial for obtaining more accurate DOA estimates. The developed cascade-connected model is validated using real data from a rectangular antenna array. Improvements in terms of accuracy and reliability are obtained and compared with the MUSIC algorithm. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]