Acceptability is one of the General Language Understanding Evaluation Benchmark (GLUE) probing tasks proposed to assess the linguistic capabilities acquired by a deep-learning transformer-based language model (LM). In this paper, we introduce the Spanish Corpus of Linguistic Acceptability EsCoLA. EsCoLA has been developed following the example of other linguistic acceptability data sets for English, Italian, Norwegian or Russian, with the aim of having a complete GLUE benchmark for Spanish. EsCoLA consists of 11,174 sentences and their acceptability judgements as found in well-known Spanish reference grammars. Additionally, all sentences have been annotated with the class of linguistic phenomenon the sentence is an example of, also following previous practices. We also provide as task baselines the results of fine-tuning four different language models with this data set and the results of a human annotation experiment. Results are also analyzed and commented to guide future research. EsCoLA is released under a CC-BY 4.0 license and freely available at https://doi.org/10.34810/data1138.