Generally, discontinuity detection in automated visual testing consists of two steps: identification of potential discontinuities using image processing techniques and classification of potential discontinuities into discontinuities and regular structures (false alarms) using a pattern recognition methodology. In the second step, since several features can be extracted from the potential discontinuities, a feature selection must be performed. In this paper, several known classifiers are studied in automated visual testing: threshold, euclidean, mahalanobis, polynomial, support vector machine and neural network classifiers. First, the performance of the classifiers is assessed individually. Second, the classifiers are combined in order to improve their performance. Seven fusion strategies in the combination were tested: and, or, majority vote, product, sum, maximum and median. The proposed methodology was tested on real data acquired from 50 noisy radiographic images of aluminum wheels, where 23 000 potential discontinuities (with only 60 real discontinuities) were segmented and 405 features were extracted for each potential discontinuity. Using fusion of classifiers, a very good performance was achieved, yielding a sensitivity of 100% and specificity of 99.97%.