In color management applications, it is essential to know the color responses of observers to arbitrary spectral radiances so that objective colorimetric quantities can be determined for use in quantitative color-matching applications. These spectral responses are typically transformed to color matching functions (CMFs) such as for the average CIE standard observer which is commonly used for the computation of various colorimetric, perceptual, and appearance attributes. While the standard CIE CMFs for the average observer have been extremely useful for this purpose, it is well-known that there is significant variation in the spectral response amongst color-normal observers. For color-critical applications, there is widespread interest in determining individual-observer color matching functions with minimal knowledge of field-of-view, age, state-of-adaptation, and other viewing conditions in the actual use-setting. By combining eigenvector analysis of CMF datasets with simple individualobserver metameric color matching exercises and multidimensional reconstruction, individual-observer CMFs can be predicted, transformed, and profiled for color-managed workflow.