The purpose of automatic segmentation is to extract interesting regions and contours from a digital image. Today a very large number of segmentation algorithms are available, whose efficiency is usually domain-dependent, i.e., they operate to different degrees of accuracy according to the parameters used which are tuned to specific application domains. A method for result evaluation and error detection in automatic segmentation is proposed. A mathematical and a physical description of possible errors are presented, and an algorithm for error detection is implemented. Three types of segmentation errors are analyzed: undersegmentation errors, oversegmentation errors, and boundary errors. An undersegmentation error occurs when pixels belonging to different semantic objects are grouped into a single region. Such errors are the most dang erous because they can invalidate the whole segmentation process. The oversegmentation error, on the contrary, occurs when a single semantic object is subdivided by segmentation into several regions. Small oversegmentation errors may be acceptable in many applications (especially in the medical field), as they can easily be rectified by merging object parts. A boundary error consists in a discrepancy between the boundaries of a semantic object and those of the segmented one. In real images, all these errors may often be encountered at the same time. The system implemented permits one to detect each type of error, at the pixel level, by referring to a manually segmented image obtained by an expert. It produces a report on segmentation results, for both a whole image and single regions.