In this work, we investigate the use of a digital camera for colorimetry. Our system consists of a measurement device and a corresponding calibration mapping. The goal is to design a system that will accurately assess the color of a sample. We develop two colorimetry systems by applying model-based and regression-based techniques. For both systems, the measurement device is formed by a digital camera and a set of filters. The term multi-exposure refers to the multiple snapshots taken by the camera along with filters. The calibration mapping which consists of matrices then takes these filtered camera RGB outputs, and returns the CIE XYZ tristimulus values under several pre-selected illumination conditions. For the model-based system, a model for the measurement device is employed; and our objective is to find the optimal filters and the corresponding calibration sets that minimize a cost function which accounts for errors in L*a*b* space, system robustness, and filter smoothness. For the regression-based system, no modeling technique is applied to the measurement device. The objective is simply to find the optimal calibration matrices that minimize the total least squared errors of a given color set in CIE XYZ coordinates under several pre-selected illumination conditions. We apply both types of colorimetry systems to two specific tasks: general purpose measurement of color samples and colorimetry of human teeth. We present experimental results for both applications. Finally, in order to measure the parameters for these systems and evaluate their performance, we had to develop special instrumentation. We will briefly describe this effort as well.