In the paper, we empirical compare the performance of neural nets and decision trees based on a data set for the detection of defects in welding seams. The data set was created by image feature extraction procedures working on x-ray images. We consider our data set as highly complex and containing imprecise and uncertain data’s. We explain how the data set was created and what kinds of features were extracted from the images. Then, we explain what kind of neural nets and induction of decision trees were used for classification. We introduce a framework for distinguishing classification methods. We observed that the performance of neural nets is not significant better than the performance of decision trees if we are only looking for the overall error rate. We found that more detailed analysis of the error rate is necessary in order to judge the performance of the learning and classification method. However, the error rate can not be the only criteria for the comparison between the different learning methods. It is a more complex selection process that involves more criteria’s that we describe in the paper.