In this paper we introduce a biologically-inspired spatial video filtering chip and discuss its application in wavelet filter banks. Two types of spatial filtering chips have been developed - the thin film analog image processor (TAIP) and the switched-capacitor analog image processor (SCAIP). Each chip can filter video at high frame rates with Gaussian-like filters having adjustable widths. By linearly combining the outputs of a bank of spatial filter chips we can create a large variety of filters. Effective use of the filtering chips requires two things. First, an assessment of the filters that can be realized within the constraints of the hardware is required. Although any function within reasonable constraints can be decomposed into a combination of Gaussian functions, an efficient method to do so is an open problem. We have restricted ourselves to a simpler problem - given a limited number of Gaussian-like functions, what useful classes of filters can be generated? Second, given an image-processing application, a method to organize a choice of filters is needed. We are currently investigating these problems in the context of feature analysis/discrimination, and have found a useful organizing principle in the continuous wavelet transform (CWT).