Hue plane preserving color correction (HPPCC), introduced by Andersen and Hardeberg [Proceedings of the 13th Color and Imaging Conference (CIC) (2005), pp. 141–146], maps device-dependent color values (RGB) to colorimetric color values (XYZ) using a set of linear transforms, realized by white point preserving 3×33×3 matrices, where each transform is learned and applied in a subregion of color space, defined by two adjacent hue planes. The hue plane delimited subregions of camera RGB values are mapped to corresponding hue plane delimited subregions of estimated colorimetric XYZ values. Hue planes are geometrical half-planes, where each is defined by the neutral axis and a chromatic color in a linear color space. The key advantage of the HPPCC method is that, while offering an estimation accuracy of higher order methods, it maintains the linear colorimetric relations of colors in hue planes. As a significant result, it therefore also renders the colorimetric estimates invariant to exposure and shading of object reflection. In this paper, we present a new flexible and robust version of HPPCC using constrained least squares in the optimization, where the subregions can be chosen freely in number and position in order to optimize the results while constraining transform continuity at the subregion boundaries. The method is compared to a selection of other state-of-the-art characterization methods, and the results show that it outperforms the original HPPCC method.