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This paper presents a methodology that brings together a number of techniques in the fields of image processing and pattern recognition with the purpose of achieving the automated detection of impact craters on images of planetary surfaces. The modular approach adopted for its development includes a phase of candidate selection, followed by template matching, in which the probability associated to each detection is established, and finally, by the analysis of the probability volume, in which the identification of craters on the image is achieved. It is tested on a set of images from four different regions of the surface of the planet Mars, all obtained by the same sensor in the last decade. The recognition rates for craters with radii that are larger than five pixels are very good, both globally and for each of the individual areas. The performance of the algorithm in the face of the variation of some of its parameters is analyzed and discussed in detail. We believe that this is a tool that is suitable for a general application in any area of a planet or satellite captured in an image, whatever the geomorphological setting, the optical sensor, and the conditions of illumination are.