Abstract This text describes our generic approaches to monitoring and prognostic, emphasizing the application of learning techniques, and focuses on model-free specific supervision entities that can be realized by a learning-from-examples method. All necessary tools for the generation of a supervised learning of a process situation classifier will be outlined. Statistical feature selection and inductive numerical learning constitute the basis for the proposed architecture. A particular supervised nonparametric learning method, developed in-house, the Q ∗ -algorithm will be presented. Practical experiments for the monitoring of a lathe are carried out.