In this paper, we introduce a circle detection technique named Hough transform to automatically recognize the corrosion pits in microscopic images. All the points in the input image are transformed into a parameter space, which is represented by a two-dimensional accumulative array with the same size of the original image. Local extreme values in the accumulative array, which represent the candidates of corrosion pits, are located using a maxima searching algorithm. The accuracy of detecting the number, radius and coordinate of pits from simulated images was examined. The results show that more than 95% of pits were successfully detected and the average errors of radius and coordinate are less than 10%, while these errors have negligible effect on the pit size distribution. The introduced method can also differentiate pits from scratches or inclusions, as indicated by the 100% accuracy of pit detection, from the simulated images presented in this study. Therefore, it is believed that the gradient-based Hough transform is a powerful method for the recognition of corrosion pits in microscopic images, making the statistical analysis of pit size and pit locations easier and more efficient. (C) 2016 Elsevier B.V. All rights reserved.