Epilepsy is a neurological disorder characterised by unusual brain activity widely known as seizure affecting 4-7% of the world's population. The diagnosis of this disorder is currently based on analysis of the electroencephalography (EEG) signals in the time-frequency domain. The analysis is performed applying various algorithms that yield high performance, however the challenge of effective real-time epilepsy diagnosis persists. To address this, we have developed a Field Programmable Gate Array (FPGA) based solution for the classification of generalized and focal epileptic seizure types using a feed-forward multi-layer neural network architecture (MLP ANN). The neural network algorithm is trained, validated and tested on 822 captured signals from Temple University Hospital Seizure Detection Corpus (TUH EEG Corpus) database. Inputs into the system were five main features obtained from EEG signals by time-frequency analysis followed by Continuous Wavelet Transform (CWT) and subsequent statistical analysis. Out of the total number of samples, 583 (70 %) of them were utilised during the system development in MATLAB and TensorFlow and 239 (30 %) samples were further used for subsequent testing of the model performance on the FPGA. Subsequently, the adequate parameters of the ANN model were determined by using k-Fold Cross-Validation. Finally, the best performing ANN model in terms of average validation data accuracy achieved during cross-validation was implemented on the FPGA for real-time seizure classification. The digital ANN solution was coded in Very High-Speed Integrated Circuit Hardware Description Language (VHDL) and tested on the FPGA using 30 % reaming data. The results of this research demonstrate that epilepsy diagnosis with quite high accuracy (95.14 %) can be achieved with (5-12-3) MLP ANN implemented on FPGA. Also, the results show the steps towards appropriate implementation of ANN on the FPGA. These results can be utilised as the basis for the design of an application-specific integrated circuit (ASIC) allowing large serial production.