In industrial quality inspection of colour texture surfaces, such as ceramic tiles or fabrics, it is important to maintain a consistent colour shade or tonality during production. We present a multidimensional histogram method using a novelty detection scheme to inspect the surfaces. Theimagenoise,introducedbytheimagingsystem,isfound mainly to affect the chromatic channels. For colour tonality inspection, the difference between images is very subtle and comparisoninthenoisedominatedchromaticchannelsiserror prone. We perform vector-ordered colour smoothing and extract a localised feature vector at each pixel. The resulting histogram represents an encapsulation of local and global information. Principal component analysis (PCA) is performed on this multidimensional feature space of an automatically selected reference image to obtain reliable colour shadefeatures,whichresultsinareferenceeigenspace.Then unseen product images are projected onto this eigenspace and compared for tonality defect detection using histogram comparison. The proposed method is compared and evaluated on a data set with groundtrut