Measurement of surface roughness of any machining process is crucial for obtaining a component or part of the correct size and surface finish in the first instance, in order to minimize the manufacturing cost. In-process monitoring of machining processes based on an estimation of the surface roughness using the cutting parameters is inaccurate. In this investigation, a fuzzy inference system based on an intelligent sensor fusion model has been developed for the purpose of in-process indirect measurement of surface roughness for a machining process. In the proposed technique, measurement of the Speed Force component, Radial Force component, Feed Force component, Vibration, and Acoustic Emission sensor inputs from a turning process have been considered as the inputs. The results have been compared with the surface roughness estimated with a second order regression model using cutting parameters as inputs. The proposed method has shown considerable improvement in the surface roughness estimation in a simulation environment.