In this paper we present a novel MLP -type neural network based on hyperbolic numbers-the Hyperbolic Multilayer Perceptron (HMLP). The neurons of the HMLP compute 2D-hyperbolic orthogonal transformations as weight propagation functions. The HMLP can therefore be seen as the hyperbolic counterpart of the known Complex MLP. The HMLP is proven a universal approximator. Furthermore, a suitable Backpropagation algorithm for it is derived. It is shown by experiments, that the HMLP can learn tasks with underlying hyperbolic properties much more accurately and efficiently than a Complex MLP and an ordinary MLP.