Source localization from MEG data in real time requires algorithms which are robust, fully automatic, and very fast. We present two neural network systems which are able to localize a single dipole to reasonable accuracy within a fraction of a millisecond, even when the signals are contaminated by considerable noise. The first network is a multilayer perceptron (MLP) which takes the sensor measurements as inputs, uses two hidden layers, and generates Cartesian source location coordinates as outputs. After training with random dipolar sources contaminated by real noise, localization of a single dipole could be performed within 300 microseconds on an 800 Mhz Athlon workstation, with an average localization error of 1.15 cm. To improve the accuracy to 0.28 cm, one can apply a few iterations of conventional Levenberg-Marquardt (LM) minimization using the MLP's output as the initial guess. The combined method is about twenty times faster than multistart LM localization with comparable accuracy. In a second network with only one hidden layer, the outputs were the amplitudes of 193 evenly distributed Gaussian functions holding a soft distributed representation of the dipole location. We trained this network on dipolar sources with real noise, and externally converted the network's output into an explicit Cartesian coordinate representation of the dipole location. This new network had an improved localization accuracy of 0.87 cm, while localization time was lengthened to about 800 microseconds.