In this work we will give explicit formulae for the application of Rosen’s gradient projection method to SVM training that leads to a very simple implementation. We shall experimentally show that the method provides good descent directions that result in less training iterations, particularly when large precision is wanted. However, a naive kernelization may end up in a procedure requiring more KOs than SMO and further work is needed to arrive at an efficient implementation.