 This paper proposes five artificial neural networks, ANNs, to estimate the compressive strength of concrete at different ages. The ANNs were trained using data from 175, 210, and 280 kGF slash CM superscript 2 mixtures collected from certified laboratories in the city of Jane. The ANNs were then tested on unseen data from other laboratories. The results showed that the networks obtained an average error of 4.69%, indicating their effectiveness and validity for use in quality control in the construction industry. This article was authored by Jose Manuel Palomino Ojeda, Stefano Rosario Boca Negra and Lenin Quiñones Huatongari.