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Several machine learning (ML) algorithms in combination with natural language pro- cessing (NLP) techniques have been used in recent years in a promising way for the auto- matic classification of software requirements. Nevertheless, several works have focused on the English language. Due to the lack of work in the Spanish language, we performed a con- trolled experiment using ML algorithms in combination with text vectorization techniques to investigate the best combination for Spanish requirements classification. Based on f1- score metrics, we found the combination of SVM with TF-IDF performs better than other combinations, with a value of 0.95 for functional and 0.79 for non-functional classification.

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