Feature extraction is one of key techniques in feature-based reverse engineering. In this paper, a novel methodology of feature extraction is presented based on collected data points of mechanical part. Firstly, regular surface is used to model individual segmented data points patch based on maximum likelihood estimate. And then the resulting surfaces are used to determine the feature primitives approximately and afterwards extract the feature parameters. Finally, Mahalanobis distance is used to evaluate the error between feature primitives and the resulting surfaces, and feature is positioned optimally utilizing a similarity transformation which minimizes the error.