Machine Learning (ML) techniques are becoming every day more effective for the analysis of large datasets in a wide range of engineering applications. This thesis particularly focuses on ML techinques for image analysis, that is, those techniques which allow data extraction from images. In order to understand the foundations of the subject of study, a general overview of the available techniques for the target application is first presented, along with a discussion of the related theoretical aspects. Second, a practical application of these techniques is targeted: the analysis of defect recognition in an industrial production process. To this, a complete workflow which includes image pre-processing, training of the ML models, and post-processing of the results, is developed.