Colour correction is the problem of mapping the sensor responses measured by a camera to the display-encoded RGBs or to a standard colour space such as CIE XYZ. In regression-based colour correction, camera RAW RGBs are mapped according to a simple formula (e.g. a linear mapping). Regression methods include least squares, polynomial and root-polynomial approaches. More recently, researchers have begun to investigate how neural networks can be used to solve the colour correction problem. _x005F_x000D_ _x005F_x000D_ In this paper, we investigate the relative performance of regression versus a neural network approach. While we find that the latter approach performs better than simple least-squares the performance is not as good as that delivered by either root-polynomial or polynomial regression. The root-polynomial approach has the advantage that it is also exposure invariant. In contrast, the Neural Network approach delivers poor colour correction when the exposure changes.