An automated classification system of pizza sauce spread using colour vision and support vector machine (SVM) was developed. To characterise pizza sauce spread with low dimensional colour features, a sequence of image processing algorithms was developed. After image segmentation from the background, the segmented image was transformed from red, green, and blue (RGB) colour space to hue, saturation, and value (HSV) colour space. Then a vector quantifier was designed to quantify the HS (hue and saturation) space to 256-dimension, and the quantified colour features of pizza sauce spread were represented by colour histogram. Finally, principal component analysis (PCA) was applied to reduce the 256-dimensional vectors to 30-dimensional vectors. With the 30-dimensional vectors as the input, SVM classifiers were used for classification of pizza sauce spread. It was found that the polynomial SVM classifiers resulted in the best classification accuracy with 96.67% on the test experiments. (C) 2004 Elsevier Ltd. All rights reserved.