Machine learning tools such as neural networks are increasingly applied in marketing and economics to learn complex relations in data. The learned relations allow machines to perform various tasks, such as recognizing objects from images or recognizing emotions from speech. This paper explores using a neural net to learn the relation between data (moments) and the parameter values of a structural economic model, so that it can “recognize,” or estimate, these parameter values from the data (moments). We train the neural net with the datasets generated by the structural model under different parameter values. The neural net can be trained to give not only the point estimates of parameters but also their statistical accuracy. We show this Neural Net Estimator (NNE) converges to meaningful and well-known limits when the number of training datasets is sufficiently large. NNE does not require computing integrals over the unobservables in the structural model. Thus, it is suitable for models where such integrals are costly in MLE/GMM. We benchmark NNE in two Monte Carlo studies. NNE is able to achieve high estimation accuracies under very light estimation costs.