Machine Learning (ML) algorithms have become a powerful instrument in software requirements classification. Nevertheless, most of the research focusing on requirements is in English, with less attention to other languages. Given a lack of datasets in Spanish language, we created a new dataset from a collection of requirements from final degree projects from the University of A Coruña. In this paper, we investigate which combinations of text vectorization techniques with ML algorithms perform best for requirements classification in Spanish dataset. We found that SVM with TF-IDF gives the highest f1-score (0.95 and 0.79 for functional and non-functional classification).