Word embeddings are useful for various applications, such as sentiment classification (Tang et al., 2014), word translation (Xing, Wang, Liu, & Lin, 2015) and résumé parsing (Nasser, Sreejith, & Irshad, 2018). Previous research has determined that word embeddings contain gender bias, which can be problematic in certain applications such as résumé parsing. This research has addressed the question whether gender bias is present in word embeddings of different languages. Gender bias has been measured on word embedding of 26 different lan- guages with the help of the Word Embedding Association Test by Caliskan, Bryson, and Narayanan (2017). The results show that most of the tested languages seem to have bias towards male, while a few languages seem to have a bias towards female. This result is in line with previous literature.