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In the food industry, many companies inspect the X-ray images of foods for foreign bodies. One promising approach for detecting food items with foreign bodies (i.e., abnormal food items) in an X-ray image is the use of image classification methods such as convolutional neural networks (CNNs), which can help automatically detect abnormal food items. One of the important issues in training a good CNN model is obtaining a large training dataset. However, it is often difficult to obtain such a dataset because it requires a manual labeling task that is time-consuming and involves a considerable amount of human effort. In addition, it is increasingly difficult to conduct a manual labeling when the food items overlap in an X-ray image. In this regard, we propose an automatic labeling method to train CNN models for detecting abnormal food items from their X-ray images. The proposed method prepares additional X-ray images that show only foreign bodies. Then, it overlaps an original X-ray image with an additional image and identifies the original X-ray image as abnormal if the overlapped area exceeds a predetermined threshold. To verify the performance of the proposed method, we conducted a case study at an X-ray inspection facility in Korea. We found that the proposed method is effective in detecting and classifying every food item in an X-ray image.