Machine Learning (ML) algorithms have become a powerful instrument in requirements classification. Several studies have implemented these techniques (Pérez-Verdejo et al., 2020), from traditional ML algorithms to Transformers, the state-of-art in Natural Language Processing (NLP). Nevertheless, several research focuses on English requirements, with less attention to other languages. Spanish is currently the second mother tongue in the world by the number of speakers (Instituto Cervantes, 2021), hence, it is important to expand the knowledge of performance classification for requirements written in Spanish. The present work aims to investigate which combinations of text vectorization techniques with ML algorithms perform best for requirements classification, using two Spanish datasets from different sources for training and testing the models.