Most digital cameras have spectral characteristics that make it impossible for the camera to provide accurate colorimetric information of a recorded scene. We extend previous work on using multiple filtered exposures by the use of a newly available capability of creating manufacturable filters using online software. Multiple criteria are considered in the determination of an optimal color filter on a training dataset. These criteria include the Vora-Value, figure of merit (FOM), training average ΔE, and training maximum ΔE. Our method includes the use of a realistic imaging noise model. We use two optimization methods: minimum mean square error (MMSE), which requires knowledge of the color sensitivities of the camera’s internal RGB filters and knowledge of the covariance matrices of the data ensemble and the noise, and a pseudoinverse method that uses only the recorded data and knowledge of the values of the training data. Filters were selected using the training data and the performance is measured on independent testing data. The results from both optimization methods show that filters chosen using the training ΔE criteria consistently outperformed the theoretical FOMs. It is also shown that the MMSE method is very sensitive to small perturbations of the internal filter sensitivities. Real camera images were recorded using the optimally determined filters from both methods. Measured errors provide confirmation of the simulated results.