We study supervised learning and generalization in coupled perceptrons trained on-line using two learning scenarios. In the first scenario the teacher and the student are independent networks and both are represented by an Ashkin–Teller perceptron. In the second scenario the student and the teacher are simple perceptrons but are coupled by an Ashkin–Teller-type four-neuron interaction term. Expressions for the generalization error and the learning curves are derived for various learning algorithms. The analytical results find excellent confirmation in numerical simulations.