Multiparametric MRI (mpMRI) is a valuable tool for the diagnosis of prostate cancer. Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of mpMRI in prostate cancer. Co-registration of diffusion-weighted and T2-weighted images is a crucial step of CAD but is not always flawless. Automated detection of poorly co-registered cases would therefore be a useful supplement. This work shows that a fully automated quality control system for co-registration of prostate MR images based on deep learning has potential for this purpose.