The automatic extraction of handwriting styles is an im- portant process that can be used for various applications in the processing of handwriting. We propose a novel method that employs hierarchical clustering to explore prominent clusters of handwriting. So-called membership vectors are introduced to describe the handwriting of a writer. Each membership vector reveals the frequency of occurrence of prototypical characters in a writer's handwriting. By clustering these vectors, consistent handwriting styles can be extracted, similar to the exemplar handwritings docu- mented in copybooks. The results presented here are chal- lenging. The most prominent handwriting styles detected correspond to the broad style categories cursive, mixed, and print.