In this paper, the performance of four different image pan-sharpening methods, the Brovey, the Gram-Schmidt (GS), the Intensity-Hue-Saturation (IHS) and the Principle Component Analysis (PCA), are investigated based on spectral and spatial distortions. In the study, the Brovey, the GS, the IHS, and the PCA pan-sharpening algorithms are applied to multispectral (MS) bands of Ikonos and QuickBird images. The spectral and spatial qualities of pan-sharpened images are tested using the Correlation Coefficient (CC), the Root Mean Square Error (RMSE), and the Structural Similarity Index (SSIM). A comparative performance analysis of the CC, the RMSE, and the SSIM shows that the PCA followed by the GS, the Brovey, and the IHS perform the best among all the techniques, except a swap in the PCA and the GS in the SSIM.