This paper is an experimental study on the robustness of the eigenfaces method for face recognition. To build a face recognition system, especially in an unconstrained surveillance system where a clear, direct, and normalized view of the face cannot be assumed, one needs to implement several image preprocessing steps like segmentation, deskewing, zooming, rotation, warping, etc., before processing the face image per se. Our aim is to determine how efficient these preprocessing steps must be in order to apply the eigenfaces method with success. The experiments are conducted on a subset of the AR-face color image database. Real images are used and altered synthetically to study the effects of 7 parameters that can be translated into corresponding preprocessing artifacts: horizontal and vertical translations, downsampling, zooming, rotation, morphing and lighting.