The concepts behind the use of multivariate image analysis (MIA) techniques for on-line monitoring of subtle features in time varying images were presented in an earlier paper (Bharati, M. H.; Macgregor, J. F. Ind. Eng. Chem. Res. 1998,37,4715). This paper illustrates the successful application of these ideas to an industrial process from the forest products industry. MIA is used to rapidly detect the presence and quantity of common lumber defects such as knots, splits, wane, pitch, and bark pockets in individual sawn lumber boards as they pass on a moving conveyor belt under a line-scan RGB camera. Multiway principal component analysis is used to decompose the acquired three-channel lumber images into a two-dimensional principal component (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of lumber defect pixels based on contrast and color information. Control charts with appropriate tolerance limits are set up to monitor counts of defective pixels lying under any defect mask in the MIA score plots for each new lumber sample as it passes through the imaging system. Those lumber samples violating the tolerance limits are automatically downgraded. This application of on-line MIA for assessing specific quality problems is not limited to lumber grading, but could be directly applied to many industrial processes involving the production of solid or heterogeneous liquid or liquid/solid products.