I don't think I yet know enough about SVMs to answer your question, but the two are quite similar. Both perceptrons and SVMs classify data by separating it with a hyperplane. I think the difference is in the learning algorithm used: perceptrons' learning method just keeps moving the plane until the good/bad points are on the right side. SVMs learning method does try to ensure that the hyperplane lies at the max margin distance from classified points. I think. :)