ABSTRACT– Multiple regression and adaptive neuro-fuzzy in ference system (ANFIS) were used to predict the surface roughness in the end milling process. Spindle speed, feed rate and depth of cut were used as predictor variables. Generalized bell me mberships function (gbellmf) was adopted during the training process of ANFIS in this study. The pr edicted surface roughness using multiple regression and ANFIS were compared with measured data, the ac hieved accuracy were 91.9% and 94% respectively. These results indicate that the tr aining of ANFIS with the gbellmf is accurate than multiple regression in the prediction of surface roughness.