We apply principal component analysis (PCA) to the characterization of artifacts in a digital image-acquisition system containing image-compression algorithms. The method is successfully applied to web cameras. The classification done with the PCA method produces three processes. The pure spatial process retrieves the luminance distribution of a static object. The pure temporal process is directly related with the temporal noise of the system. An intermediate spatial-temporal process reveals the interaction between the compression algorithms and the spatial-frequency contents of the object. Without prior information, the PCA method is able to distinguish this interaction from the classical temporal noise. The analysis of the anomalous pixels also reveals the location in the scene where the compression algorithms work harder. An extension of this analysis identifies the origin of the anomalous behavior in terms of its spatial or temporal character. © 2004 Society of Photo-Optical Instrumentation Engineers.