This study was conducted to develop a fast and accurate computer-based machine vision system for detecting irregular potatoes in real-time. Supported algorithms were specifically developed and programmed for image acquisition and processing, controlling the whole process, saving the classification results and monitoring the progress of all operations. A database of images was first formulated from potatoes with different shapes and sizes, and then some essential geometrical features such as perimeter, centroid, area, moment of inertia, length and width were extracted from each image. Also, eight shape parameters originated from size features and Fourier transform were calculated for each image in the database. All extracted shape parameters were entered in a stepwise linear discriminant analysis to extract the most important parameters that most characterized the regularity of potatoes. Based on stepwise linear discriminant analysis, two shape features (roundness and extent) and four Fourier-shape descriptors were found to be effective in sorting regular and irregular potatoes. The average correct classification was 96.5% for a training set composed of 228 potatoes and then the algorithm was validated in another testing set composed of 182 potatoes in a real-time operation. The experiments showed that the success of in-line classification of moving potatoes was 96.2%. Concurrently, the well-shaped potatoes were classified by size achieving a 100% accuracy indicating that the developed machine vision system has a great potential in automatic detection and sorting of misshapen products. (c) 2012 Elsevier Ltd. All rights reserved.