Glass fibre-reinforced polymer (GFRP) composites are alternative to engineering materials because of economic, light weight, corrosive resistance and superior properties. The experimental research under taken by the scholars is to study the impact of machining parameters on surface roughness of composite material by applying artificial neural network (ANN) and response surface method (RSM). The orthogonal turning operations were carried out on the composite material using tungsten carbide (WC) insert. During machining, the cutting speed (Vc), feed rate (fs) and depth of cut (ap) were varied. Turning experiments were designed based on the statistical three level full factorial experimental design techniques. An artificial neural network and response surface method have been developed, which can predict the surface roughness of the machined workpiece. The experimental results concur well with the results obtained from predictive model. Copyright© 2012 Praise Worthy Prize Srl-All rights reserved.