We study a simple learning model based on the Hebb rule to cope with `delayed', unspecific reinforcement. In spite of the unspecific nature of the information-feedback, convergence to asymptotically perfect generalization is observed, with a rate depending, however, in a non-universal way on learning parameters. Asymptotic convergence can be as fast as that of Hebbian learning, but may be slower. Morever, for a certain range of parameter settings, it depends on initial conditions whether the system can reach the regime of asymptotically perfect generalization, or rather approaches a stationary state of poor generalization.