Most machine learning tools aim at creating good predictions for new samples. However, obtaining 100% is not feasible in most problems, and therefore modeling the uncertainty over such predictions becomes necessary in several applications. This can be achieved by estimating conditional densities. In this work, we propose a novel method of conditional density estimation based on Fourier series and artificial neural networks, and compare it to other estimators on five distinct datasets. We conclude that our proposed method outperforms the other tested methods.