Many natural backgrounds, such as radar clutter from the ocean surface, which were previously thought to be random may be chaotic. Because of the finite dimensionality of a chaotic background, a non-linear signal processor can be trained as a global predictor. The results of a continuing study of polynomial neural nets (PNN), used for global prediction, are described. Encouraging results have been obtained with PNNs for both signal processing (time series) and images. Since PNNs can be trained to predict chaotic backgrounds, threshold target images can be detected by subtracting the predicted background from the target plus background. In this paper we summarize the basis for PNN processing and present recent PNN image processing results using as a chaotic background video images of the ocean surface.