We are mapping an image clustering algorithm onto an FPGA- based computer system. Our approach processes raw pixel data in the red, green, blue color space and generates an output image where all pixels are assigned to classes. A class is a group of pixels with similar color and location. These classes are then used as the basis of further processing to generate tags. The tags, in turn, are used to generate queries for searching libraries of digital images. We run our image tagging approach on an FPGA-based computing machine. The image clustering algorithm is run on an FPGA board, and only the classified image is communicated to the host PC. Further processing is run on the host. Our experimental system consists of an Annapolis Wildforce board with four Xilinx XC4000 chips and a PCI connection to a host PC. Our implementation allows the raw image data to stay local to the FPGAs, and only the class image is communicated to the host PC. The classified pixels are then used to generate tags which can be used for searching a digital library. This approach allows us to parallelize the image processing on the FPGA board, and to minimize the data handled by the PC. FPGA platforms are ideally suited for this sort of initial processing of images. The large amount of image data can be preprocessed by exploiting the inherent parallelism available in FPGA architectures, keeping unnecessary data off the host processor. The result of our algorithm is a reduction by up to a factor of six in the number of bits required to represent each pixel. The output data is passed to the host PC, thus reducing the processing and memory resources needed compared to handling the raw data on the PC. The process of generating tags of images is simplified by first classifying pixels on an FPGA-based system, and digital library search is accelerated.