Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as \(XYZ\), is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a \(3 \times 3\) matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson al., to perform a perceptual color characterization. In particular, we search for the \(3 \times 3\) matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE \(\Delta E\) error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3for the \(\Delta E\) error, 7& for the S-CIELAB error and 13% for the CID error measures.