Method for monitoring a joining process of workpieces in an automatic joining machine, wherein in a training phase: precursor workpieces are joined in the automatic joining machine and/or in at least one other automatic joining machine by means of the joining process and hereby a digital process image of the joining process, which is formed from at least one predetermined process parameter and at least one measured and/or calculated process variable, is recorded for the respective precursor workpiece, wherein the precursor workpieces are the same type of workpiece type as the workpieces, and a test signal, which correlates with a joining quality of the joined workpieces, is determined on the joined precursor workpieces by means of a predetermined testing method, and a quality indication signaling the determined joining quality is generated by means of an evaluation method, and an estimation model of the machine learning is generated by means of a predetermined training method, which receives the respective digital process image as input and assigns an estimated quality indication to the input as output, which is trained in accordance with the signaled quality indication, and in an operating phase, in which the automatic joining machine joins the workpieces by means of the joining process: the digital process image of the joining process of the respective workpiece is determined and supplied to the estimation model as input and the estimated quality indication relating to the respective joined workpiece is generated by means of the estimation model, and for the workpieces joined by the automatic joining machine, a rejection measure is triggered for rejecting those workpieces for which the estimated quality indication meets a predetermined rejection criterion. in a training phase: precursor workpieces are joined in the automatic joining machine and/or in at least one other automatic joining machine by means of the joining process and hereby a digital process image of the joining process, which is formed from at least one predetermined process parameter and at least one measured and/or calculated process variable, is recorded for the respective precursor workpiece, wherein the precursor workpieces are the same type of workpiece type as the workpieces, and a test signal, which correlates with a joining quality of the joined workpieces, is determined on the joined precursor workpieces by means of a predetermined testing method, and a quality indication signaling the determined joining quality is generated by means of an evaluation method, and an estimation model of the machine learning is generated by means of a predetermined training method, which receives the respective digital process image as input and assigns an estimated quality indication to the input as output, which is trained in accordance with the signaled quality indication, and in an operating phase, in which the automatic joining machine joins the workpieces by means of the joining process: the digital process image of the joining process of the respective workpiece is determined and supplied to the estimation model as input and the estimated quality indication relating to the respective joined workpiece is generated by means of the estimation model, and for the workpieces joined by the automatic joining machine, a rejection measure is triggered for rejecting those workpieces for which the estimated quality indication meets a predetermined rejection criterion. the digital process image of the joining process of the respective workpiece is determined and supplied to the estimation model as input and the estimated quality indication relating to the respective joined workpiece is generated by means of the estimation model, and for the workpieces joined by the automatic joining machine, a rejection measure is triggered for rejecting those workpieces for which the estimated quality indication meets a predetermined rejection criterion.