Handwriting recognition enables the automatic transcription of large volumes of digitized collections, providing access to the content. However, regardless of the system used, some recognition errors still oc-cur. With the advancement of Large Language Models (LLMs), the ques-tion arises whether these models can improve handwriting recognition as a post-processing step. We have developed a method for LLM-based post-correction and evaluated it on three benchmark datasets, namely Washington, Bentham, and IAM. We consistently achieved a character error rate reduction of up to 30%, though we observed significant vari-ability depending on the prompt and the LLM used.