Abstract-The existing manual point counting technique for ferrite content measurement is a difficult time consuming method which has limited accuracy due to limited human perception and error induced by points on boundaries of grid spacing. In this paper, we present a novel algorithm, based on image analysis and pattern classification, to evaluate the volume fraction of ferrite in microstructure containing ferrite and austenite. The prime focus of the proposed algorithm is to solve the problem of ferrite content measurement using automatic binary classification approach. Classification of image data into two distinct classes, using optimum threshold finding method, is the key idea behind the new algorithm. Automation of the process to measure the ferrite content and to speed up specimen's testing procedure is the main feature of the newly developed algorithm. Improved performance index by reducing error sources is reflected from obtained results and validated through the comparison with a well-known method of Ohtsu.