With the maturity of millimeter-wave devices, millimeter-wave imaging radar has been applied to human security check. However, the localization of forbidden objects in millimeter-wave images is still a difficult task, which greatly limits the application of millimeter-wave imaging radar. This paper adopts convolution neural network (CNN) to automatically localize forbidden objects, such as guns and knives, in millimeter-wave images. A sliding window is applied to slide over the input image. Then the probability of the existence of forbidden object in the image patch can be obtained via CNN. The image patches are overlapped with each other, and the probability values of all image patches are accumulated to obtain the probability accumulation map (PA-map). The PA-map reflects the position of forbidden objects. Due to the application of CNN and PA-map, this method achieves a high accuracy of localization in the experiment, which verifies the effectiveness of this method.