Artificial Neural networks have been provided an effective approach for EEG signals because of its self-adaption and natural way to organize. Artificial intelligence system based on the qualitative diagnostic criteria and decision rules of human expert could be useful as the clinical decision supporting tool for the localization of epileptogenic zones and the training tool for u experienced clinicians. Also, considering the fact that experiences from the different clinical fields must be cooperated for the diagnosis of epilepsy, integrated artificial intelligence system will be useful for the diagnosis and treatment of epilepsy patients. This research presents an automated system that can diagnose epilepsy. The system is composed of two phases. The first phase is the features extraction by using discrete wavelet transform (DWT). The second phase is the classification of the EEG signals (existence of epileptic seizure or not), using artificial neural networks. The proposed system will help and aid the the neurologists to detection of the epileptic activity.