Many studies on machine learning (ML) for computer-aided diagnosis are restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. The aim of this work is to develop a convolutional neural network (CNN) for the automatic QC of 3D T1w brain MRI for a large heterogeneous clinical data warehouse. Specifically, the objectives were: 1) to identify images which are not proper T1w brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy, similar to the human raters. For objective 3, the performance was good but substantially lower to that of human raters.