Castamide, a kind of casting polyamide, is widely used in industry because of its light weight and high corrosion resistance, and because of its impact-resistant, oil-free and silent operation. The scope of its usage has been increasing. It is used in the packaging, textiles, chemicals, leather, construction and heavy machinery manufacturing sectors. Particularly, in the manufacture of machine parts like gears surface roughness of which is crucial, it has superseded many metals because it is important to be able to predict the surface roughness to get more qualified materials. The aim of this study is to predict the surface roughness of Castamide material after machining process using ANN (artificial neural network). In this study, experiments on Castamide were done in CNC milling using high speed steel and hard metal carbide tools. The cutting parameters (cutting speed, feed rate and depth of cut) were changed and the average surface roughness (Ra, μm) values were obtained. In the experiments, the effects of cutting tools with the same diameters, but with different cutting edges and tool materials on average surface roughness were also investigated. The data were used to train and test a dynamic ANN model. It is quite clear from the model results that the surface roughness predicted by the ANN model matches well with the training data as well as the test data. The developed model has managed to is to the surface roughness with correlation rate of 83.6% and minimum error rate of 0.02.