A volume holographic wavelet correlation processor is proposed and constructed for correlation identification. It is based on the theory of wavelet transforms and the mechanism of angle-multiplexing volume holographic associative storage in a photorefractive crystal. High parallelism and discrimination are achieved with the system. Our research shows that cross-talk noise is significantly reduced with wavelet filtering preprocessing. Correlation outputs can be expanded from one dimension in a conventional system to two dimensions in our system. As a result, the parallelism is greatly enhanced. Furthermore, several advantages of wavelet transforms in improving the discrimination capability of the system are described. The conventional correlation between two images is replaced by wavelet correlation between main local features extracted by an appropriate wavelet filter, which provides a sharp peak with low sidelobes. Theoretical analysis and experimental results are both given to support our conclusions. its preliminary application to human-face recognition is studied.