In this paper, we propose Kernel Principal Component Analysis as a feature selection method for offline cursive handwriting recognition based on Hidden Markov Models. In contrast to formerly used feature selection methods, namely standard Principal Component Analysis and Independent Component Analysis, nonlinearity is achieved by making use of a radial basis function kernel. In an experimental study we demonstrate that the proposed nonlinear method has a great potential to improve cursive handwriting recognition systems and is able to significantly outperform linear feature selection methods. We consider two diverse datasets of isolated handwritten words for the experimental evaluation, the first consisting of modern English words, and the second consisting of medieval Middle High German words.