This paper documents two sequence-labeling models for NER in Spanish: a conditional random field model with handcrafted features and a BiLSTM-CRF model with word and character embeddings. Both models were trained and tested using CAPITEL (an annotated corpus of newswire written in European Spanish) and were submitted to the shared task on Spanish NER at IberLEF 2020. The best result was obtained by the CRF model, which produced an F1 score of 84.39 on the test set and was ranked #6 on the shared task.