A problem during manufacturing of body parts in the automobile industry is the frequent occurrence of surface cracks in sheet metal cold forming processes. In this paper, we compare different supervised data mining techniques to predict cracks in deep-drawn sheet metals using their flange lengths as correlating features. Inline images of sheet metals are taken during the deep drawing process through cameras that are installed in every stage of a six-stage press line. A commercial software is used to label the images as defective and non-defective. Additionally, flange lengths, which generally correlate with forces set at the machines, are measured along the periphery of the sheets. The results are promising, as the models achieved satisfactory accuracy rates, albeit with some margin for improvement. This paper aims to choose a binary classification algorithm that is best suitable for the given dataset. Furthermore, the results provide an insight on the correlation of flange lengths with the occurrence of cracks, which can be used for online parameter adjustments of the machines in the future.