Machine learning interpolation of atomic potential energy surfaces enables the nearly automatic construction of highly accurate atomic interaction potentials. Here we discuss the Behler–Parrinello approach that is based on artificial neural networks (ANNs) and detail the implementation of the method in the free and open-source atomic energy network (ænet) package. The construction and application of ANN potentials using ænet is demonstrated at the example of titanium dioxide (TiO$_2$), an industrially relevant and well-studied material. We show that the accuracy of lattice parameters, energies, and bulk moduli predicted by the resulting TiO$_2$ ANN potential is excellent for the reference phases that were used in its construction (rutile, anatase, and brookite) and examine the potential’s capabilities for the prediction of the high-pressure phases columbite (α-PbO$_2$ structure) and baddeleyite (ZrO$_2$ structure).