Due to volume change effects of concrete, understanding the mechanisms of shrinkage has become an important point for reducing cracks and, consequently, the penetration of deleterious agents into concrete structures. Despite the increase in experimental studies on concrete drying and autogenous shrinkage there is still a need to develop new analytical and numerical methods to predict shrinkage supporting the design of concrete structures. This study proposed an Artificial Neural Network (ANN) model to predict the concrete drying shrinkage. A national database containing 689 experimental shrinkage data records, in more than 90 different mixtures of conventional concrete was constructed, in accordance with NBR 16834. The model had as input data for predicting shrinkage the consumption and type of cement, retarding and plasticizer additive, shrinkage compensator, water/cement ratio and age of concrete. The model presented coefficients of determination(R²) for training and test data above 0,998 and 0,906, proving that the model is an important tool for predicting drying shrinkage for decision making during the initial study in the design phase and concrete mix design.