I will present a simple first order model of how variation in illumination affects the output of Filter Response Filters (FRF). FRF are of interest because: (a) they are commonly used as texture features in automated texture classification systems, and (b) they are typically proposed as the "back pocket model" of the first stage of the human visual system. I'll show how naïve classifiers built using these simple features can fail, and how the model can be used to produce a classifier that is robust to illumination variation. What this will show is that single still images are not often not sufficient for the purposes of surface classification - either for human or automated systems. I'll conclude by describing some of our recent research that is investigating our perceptions of surface texture.