In the case of thermographic inspection, the workpiece is heated in a particular manner followed by the observation of the resulting temperature increase at the material surface by means of an infrared camera. Inhomogeneities such as surface cracks cause a nonuniform distribution of the temperature; consequently, they can be localized in the infrared images. For metallic pieces, the most efficient way is inductive heating, whereby the induced eddy current generates heat directly in the surface skin of the sample. Experiments have been carried out on how steel workpieces, especially castings, can be thermographically inspected to detect cracks. The testing is a nondestructive and contact-free method. The goal is to develop a fully automated testing equipment with high throughput, where the flawed pieces are identified by evaluation and classification of the infrared images. The classification task is to distinguish between temperature increase around a crack and additional heating at the edges of the workpieces. Neural network has been used to train and to classify about 750 images, and good results have been achieved.