This thesis is on the use of a surface reconstruction method called Photometric Stereo in Machine Vision. In the first part we propose a modification of the method which allows one to reconstruct the shape and colour of the surface in the presence of highlights and shadows. We also provide an extensive error analysis of the algorithm, concentrating on design issues such as choice of thresholds. The second part of the thesis is on the subject of illumination direction invariant classification of 3-dimensional textures. Using Photometric Stereo, one can reconstruct the surface shape and infer its statistical properties. The learned statistics can be used to predict the behaviour of a class of texture features, which in its turn allows one to recognise a 3-dimensional texture under unknown illumination.