One approach to camera characterization is to attempt to recover the spectral properties of the surfaces in a scene and then compute the tristimulus values from these estimated reflectances. This paper addresses the question of whether such spectral-based characterization methods can outperform traditional characterization methods. In this paper we have evaluated three different techniques for camera characterization that employ multispectral methods. The Imai and Berns method and the Hardeberg method are based on the use of a linear model of reflectance with three basis functions whereas the Shi and Healey method allows the use of a higher dimensional linear model. The characterization performance (median ΔE) of the techniques using the full set training samples was found to be 3.69, 4.26 and 3.55 respectively for the Imai and Berns method, the Hardeberg method and the Shi and Healey method. In a previous study we found that polynomial and neural-network methods are able to perform characterization on the same data with a median ΔE of 2.02 and 2.01 respectively. We find no evidence, therefore, that multispectral imaging techniques provide any advantage over traditional characterization methods for a three-channel camera imaging under a single illuminant. Further work is required to evaluate multispectral techniques for multiple imaging under more than one light source and for cameras with more than three color channels.