In this work we present a framework for recognizing writer for a handwritten word. We make use of allographic features at sub-word level. Our work is motivated by previous techniques which make use of a codebook. However, instead of encoding the features using the code-words, we exploit the discriminative properties of features that belong to the same cluster, in a supervised approach. We are able to achieve writer identification rates close to 63% on the handwritten words drawn from a dataset by 10 writers. Our work has application in scenarios where multiple writers write/annotate on the same page. © Springer International Publishing AG 2017.