In the present research, electrical discharge machining (EDM) of carbon fiber reinforced plastic (CFRP) material was studied. The selection of optimum electrical discharge machining parameters combinations for the purpose of obtaining higher cutting efficiency and accuracy is a challenge task due to the presence of a large number of process variables. This paper presents an attempt to develop an appropriate machining strategy for a maximum process criteria yield. A feed-forward back-propagation neural network was developed to model the machining process. The three most important parameters-material removal rate, tool electrode wear rate and surface roughness-were considered as measures of the process performance. A large number of experiments were carried out over a wide range of machining conditions to study the effect of input parameters on the machining performance. The experimental data was used for the training and verification of the model. Testing results demonstrated that the model is suitable for predicting the response parameters accurately as a function of most effective control parameters, i.e. pulse duration, peak current and tool electrode rotational speed.