In this work we propose two hybrid NN/HMM systems for handwriting recognition. The tied posterior model approximates the output probability density function of a Hidden Markov Model (HMM) with a neural net (NN). This allows a discriminative training of the model. The second system is the tandem approach: A NN is used as part of the feature extraction, and then a standard HMM apporach is applied. This adds more discrimination to the features. In an experimental section we compare the two proposed models with a baseline standard HMM system. We show that enhancing the feature vector has only a limited effect on the standard HMMs, but a significant influence to the hybrid systems. With an enhanced feature vector the two hybrid models highly outperform all baseline models. The tandem approach improves the recognition performance by 4.6% (52.9% rel. error reduction) absolute compared to the best baseline HMM.