Five transformations of RGB (red, green, and blue) colour space were evaluated for their performance in classifying pizza toppings, i.e., NRGB (normalised RGB), HSV (hue, saturation, and value), I1I2I3, L*a*b*, and YCbCr. Using these five colour space transformations, the performance of three SVM (support vector machine) classifiers (linear, polynomial, and RBF) on pizza topping classification was compared with two classical classification approaches, i.e., C4.5 classifier and an RBF_NN (radial basis function neural network) classifier. The C4.5 classifier obtained the best classification accuracy of 93.3% with L*a*b* or I1I2I3 colour space transformation, and the RBF_NN classifier achieved the best classification accuracy of 86.7% with YCbCr, HSV or L*a*b* colour space transformation. For the SVM classifiers, the polynomial SVM classifier had the best classification accuracy of 96.7% with HSV colour space transformation, while the radial basis function (RBF) SVM classifier obtained the best classification accuracy of 90.0% with YCbCr, L*a*b* or HSV colour space transformation. Among the SVM classifiers, the polynomial SVM classifier combined with HSV colour space transformation proved to be a good approach for the classification of pizza toppings using computer vision. (C) 2004 Elsevier Ltd. All rights reserved.