In this paper we describe a system for the recognition of cursively handwritten words, using Hidden Markov Models. The 26 words of our vocabulary were selected by constructing and analyzing a grammar that defines the German amounts lower than one million. We collected 13000 words of 500 different writers. Using a new perturbation approach instead of conventional normalization we could increase our recognition rate significantly on this database.