A manufacturing process, including in the field of electronics, must be organized in such a way as to ensure the production of quality items. This can be achieved by using and following certain quality control procedures and methods. Quality control is usually carried out at several stages of manufacturing to check that there are no deviations in product parameters, thus to ensure compliance with the production specification. Recently, in the scope of concepts related to industry 4.0, techniques from machine learning and artificial intelligence have also been applied, making quality control systems increasingly intelligent, thereby greatly assisting quality experts. The aim of the paper is to map the current scientific achievements regarding the application of machine learning in the field of quality control through bibliometric analysis and to present the results of performed experiments with data from a real manufacturing process. At the created predictive models that solve a classification task with two classes pass or fail the quality check, the following learning methods are applied: (a) artificial neural network with optimization of parameters, (b) principal component analysis and deep learning, (c) Random Forest. Performance of the three learning methods is high as the second method is particularly suitable.