We introduce a new supervised learning model that is a nonhomogeneous Markov process and investigate its properties. We are interested in conditions that ensure that the process converges to a “correct state,” which means that the system agrees with the teacher on every “question.” We prove a sufficient condition for almost sure convergence to a correct state and give several applications to the convergence theorem. In particular, we prove several convergence results for well-known learning rules in neural networks.