This paper presents the nonlinear black box modeling of a hydraulic translatory drive using neural networks. The type of neural network employed here is the multilayer perceptron. Feeding previous inputs and outputs into the network leads to two different black box model structures, namely the series-parallel and the parallel model. Their suitability for modeling the hydraulic drive on the basis of measurements on a test bed is compared.