The classification of historical and contemporary novels is a nuanced task that has traditionally relied on expert literary analysis. This paper introduces a novel dataset comprising Danish and Norwegian novels from the last 30 years of the 19 th century, annotated by literary scholars to distinguish between historical and contemporary works. While this manual classification is time-consuming and subjective, our approach leverages pre-trained language models to streamline and potentially standardize this process. We evaluate their effectiveness in automating this classification by examining their performance on titles and the first few sentences of each novel. After fine-tuning, the models show good performance but fail to fully capture the nuanced understanding exhibited by literary scholars. This research underscores the potential and limitations of NLP in literary genre classification and suggests avenues for further improvement, such as incorporating more sophisticated model architectures or hybrid methods that blend machine learning with expert knowledge. Our findings contribute to the broader field of computational humanities by highlighting the challenges and opportunities in automating literary analysis.