Stylometric authorship attribution is concerned with the task of assigning texts of unknown, pseudonymous or disputed authorship to their most likely author, often based on a comparison of the frequency of a selected set of features that represent the texts. The parameters of the analysis, such as feature selection and the choice of similarity measure or classification algorithm, have received significant attention in the past. Two additional key factors for the performance and reliability of stylometric methods, however, have so far received less attention, namely corpus composition and corpus language. As a first step, the aim of this study is to investigate the influence of language on the performance of stylometric authorship attribution. We address this question using four different corpora derived from the European Literary Text Collection (ELTeC). We use machine-translation to obtain each corpus in the other three languages. We find that, as expected, the attribution accuracy varies between language-based corpora, and that translated corpora, on average, display a lower attribution accuracy compared to their counterparts in the original language. Overall, our study contributes to a better understanding of stylometric methods of authorship attribution.