This paper describes a technique to predict the fading channel of an automated guided vehicle (AGV) that moves along a pre-determined route. A probabilistic neural network (PNN) estimates the most likely signal by performing pattern matching between a stored and current fading signal window. The prediction unit is being developed as part of an anomaly detection unit that together will provide advance information on pending communication outages in a factory communications network. Multiple distributed receivers are employed in order to further improve the accuracy of the prediction. Performance is evaluated using a ray-tracing model of the moving AGV and results show that the mean squared error (MSE) can be reduced four orders of magnitude by employing eight receivers.