The Scanning N-Tuple classifier (SNT) is a fast and accurate method for classifying sequences. Applications include both on-line and off-line hand-written character recognition. SNTs have conventionally been trained using maximum likelihood parameter estimation. This paper describes a disciminative training rule that can be applied to ensembles of SNTs. Results demonstrate a significant improvement for the discriminative ensemble method. For comparison purpose we also implemented a Support Vector Machine (SVM) operating in the sequence domain. We tested each method on a chain-coded version of the MNIST hand-written digit dataset. The SNT is not quite as accurate as the SVM, but is much faster both in training and recognition.