For the conservation of historic monuments, there may be considerable value in automating the methods of detection and analysis of surface condition and deterioration. This paper describes tests using a range of multiband and multispectral images for the assessment of architectural façade cover by means of supervised image classifications. From the spectral training sets, both pairwise distances (the Euclidean distance and the Jeffries-Matusita (J-M) distance) are calculated and are used to predict the a posteriori accuracy of image classification. Furthermore, the effects of increasing the number of spectral bands (blue, green, red and near-infrared) in the supervised maximum-likelihood classification procedures are also analysed, as are the benefits of applying principal components. The resultant multiband datasets increased both the J-M distance and the classification accuracy of the architectural façade, and thus enabled better identification and recognition of the different kinds of façade-cover features.