The main objective of the present research is to find the influence of process parameters on the state variables (i.e., surface roughness and material removal rate) in Wire Electrical Discharge Machining (WEDM) of Titanium Diboride (TiB2) nanocomposite ceramics. This work adopted an L32 orthogonal array based on Taguchi method for design of experiments. Statistically evaluating the obtained data is carried out by using the analysis of variance, signal to noise and artificial neural network techniques. Then, the effects of process parameters on the surface roughness and material removal rate are studied. Finally, the Multilayer Perceptron (MLP) neural network is used to model the WEDM of TiB2 nanocomposite ceramic. The obtained results have demonstrated very good modeling capacity of the proposed neural network. Furthermore, analyses have appropriately presented the influence of process parameters on state variables.