A new approach for feature tracking on sequential satellite sensor images using neural networks has been developed. The method defines the correspondence problem between features as the minimization of a cost function using a Hopfield neural network. It has been tested on Meteosat radiometer images by tracking a cloud with rotational movement and compared to the maximum cross-correlation method. The Hopfield net was found to be more accurate and faster.