Personal nouns, i.e. common nouns denoting human beings, play an important role in manifesting gender and gender stereotypes in texts, especially for languages with grammatical gender like German. Automatically detecting and extracting personal nouns can thus be of interest to a myriad of different tasks such as minimizing gender bias in language models and researching gender stereotypes or gender-fair language, but is complicated by the morphological heterogeneity and homonymy of personal and non-personal nouns, which restrict lexicon-based approaches. In this paper, we introduce a classifier created by fine-tuning a transformer model that detects personal nouns in German. Although some phenomena like homonymy and metalinguistic uses are still problematic, the model is able to classify personal nouns with robust accuracy (f1-score: 0.94).