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The knowledge of ocean surface circulation is of major importance for many applications, including the understanding of global climate, resources exploitation, and containment of chemical spills. In this letter, sea-surface feature tracking based on the Hopfield neural network (NN) is described. The method is based on the minimization of an energy function that represents the feature tracking problem. A Hopfield NN is used to merge cross-correlation information with prior knowledge of sea-surface flows and image contextual information. It has been tested on real satellite images. A set of five Advanced Very High Resolution Radiometer thermal images of the coastal zone of California, along with a data set of coincident surface drifters positions, was used to test the method. Results of the new analysis are compared with in situ data and previous results using other techniques. The method can be used on various kinds of images for tracking and also find other applications in image registration and pattern recognition.