Most successful systems for the recognition of unconstrained handwriting currently rely on expert-crafted feature sets that compute local geometric properties from text images. However, by applying appearance based analysis techniques appropriate features could be derived from training data automatically. Therefore, in this paper several different methods for computing appearance-based feature representations are investigated and compared to the performance of a state-of-the-art writer-independent recognition system based on geometric features. In extensive experiments promising results were obtained on a challenging recognition task.