The use of wavelets has grown enormously since their original inception in the mid-1980s. Since the wavelet data representation combines spatial, frequency, and scale information in a sparse data representation, they are very useful in a number of image processing applications. This paper discusses current work in applying wavelets to object and pattern recognition. Feature extraction methods and search algorithms for matching images are discussed. Some important issues are the search for invariant representations, similarities between existing applications and the human visual system, and the derivation of wavelets that match specific targets. Results from several existing systems and areas for future research are presented.