Identifying word classes is an important part of describing a language. Research about sign languages often lack distinctions crucial for identifying word classes, e.g. the difference between sign and gesture. Additionally, sign languages typically lack written form, something that often constrains quantitative research on sign language to the use of glosses translated to the spoken language in the area. In this thesis, such glosses have been extracted from The Swedish Sign Language Corpus. The glosses were mapped to utterances based on Swedish translations in the corpus, and these utterances served as input data to a word space model, producing a co-occurence matrix. This matrix was clustered with the K-means algorithm. The extracted utterances were also clustered with the Brown algorithm. By using V-measure, the clusters were compared to a gold standard annotated manually with word classes. The Brown algorithm performs significantly better in inducing word classes than a random baseline. This work shows that utilizing unsupervised learning is a feasible approach for doing research on word classes in Swedish Sign Language. However, future studies of this kind should employ a deeper linguistic analysis of the language as a part of choosing the algorithms.