Current classification algorithms focus on vectorial data, given in euclidean or kernel spaces. Many real world data, like biological sequences are not vectorial and often non-euclidean, given by (dis-)similarities only, requesting for efficient and interpretable models. Current classifiers for such data require complex transformations and provide only crisp classification without any measure of confidence, which is a standard requirement in the life sciences. In this paper we propose a prototype-based conformal classifier for dissimilarity data. It effectively deals with dissimilarity data. The model complexity is automatically adjusted and confidence measures are provided. In experiments on dissimilarity data we investigate the effectiveness with respect to accuracy and model complexity in comparison to different state of the art classifiers.