Recent works for learning hidden Markov models in a discriminant way have focused on maximum margin training, which remains an open problem due to the lack of efficient optimization algorithms. We developed a new algorithm that is based on non convex optimization ideas and that may solve maximum margin learning of GHMMs within the standard setting of partially labeled training sets. We provide experimental results on both on-line handwriting and off-line handwriting recognition.