The majority of pansharpening methods can be classified as spectral or spatial methods, depending on whether they are based on component substitution (CS) or multiresolution analysis (MRA). So far, the suitability of one class or methods rather than another has been seldom discussed. In this paper, through experiments on IKONOS and simulated Pléiades datasets, the authors demonstrate that the performances of spectral methods depend on the extent of spectral matching, measured by the coefficient of determination (CD) of the multivariate regression between MS and P. For data with simulated P, CD is very close to one and all methods perform almost identically. For true IKONOS datasets, the CD is few percent lower and spatial methods, once they have been optimized through the knowledge of the modulation transfer function (MTF) of the imaging system, are always more performing than spectral methods. Since spatial methods are unaffected by the spectral matching, they are preferable whenever such an issue is critical, e.g., for hyperspectral pansharpening.