Physical defects reduce the organic solar cells (OSC) functioning. Throughout the OSC fabrication process, the defects can occur, for instance, by scratches or uneven morphologies. In general, bulk defects, interface defects, and interconnect defects can promote shunt and series resistance of the cell. It is crucial to properly detect and classify such defects and their amount in the structure. Correlating such defects with the performance of the cell is important both during the R&D stages to optimize processes, and for mass production stages where defects detection is an integral part of the production line. For the recognition of texture variations in the scanning electron microscope images caused by these defects is crucial the definition of a set of features for texture representation. Because the low-order Zernike moments can represent the whole shape of the image and the high-order Zernike moments can describe the detail. Then, in our case, the feature of the image can be represented by a small number of Zernike moments. In fact, the feature set extracted and described by the Zernike moments are not sensitive to the noises and are hardly redundant. So it possible concentrate the signal energy over a set of few vectors. Finally, for classification, an elliptical basis function neural networks was used. The results show effectiveness of the proposed methodology. In fact, we obtained correct classification of 89.3% over testing data set.