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This paper presents the development procedure of the feature extraction and classification module of an intelligent sorting system for electronic components. This system was designed as a prototype to recognise six types of electronic components for the needs of the educational electronics laboratories of the Kavala Institute of Technology. A list of features that describe the morphology of the outline of the components was extracted from the images. Two feature selection strategies were examined for the production of a powerful yet concise feature vector. These were correlation analysis and an implementation of support vector machines. Moreover, two types of neural classifiers were considered. The multilayer perceptron trained with the back-propagation algorithm and the radial basis function network trained with the K-means method. The best results were obtained with the combination of SVMs with MLPs, which successfully recognised 92.3% of the cases. [ABSTRACT FROM AUTHOR]