The paper presents results of merging lower-resolution spectral data (Landsat, 30m) with panchromatic images of higher spatial resolution (IRS 5.8m). During the first stage of the research, thirty methods of merging satellite data (including their variants) have been tested. The first assessment was based on statistical measures covering spectral distortion and spatial enhancement of pansharpened images. The second assessment was based on the color composite factors essential for photo interpretation. Comparing both obtained ranks of methods revealed substantial differences in their assessed spectral distortion. On the other hand, there appeared similarities in the obtained values for the spatial enhancement of pansharpened images. The reasons of such discrepancies were defined. The research allowed appointing the HPF (High Pass Filter) and LCM (Local Correlation Modeling) methods as the best according to the tested factors. In the second part of the research, the applicability of the selected methods was tested. Information content of color composites was analyzed as well as tresholding and band ratioing. In the tests there were used images fused through five merging methods: HPF, LCM, IHS (Intensity, Hue, Saturation), PCA (Principal Components Analysis) and WMK (based on band ratioing and having specific photo interpretation features). The findings of the research suggest that none of the merging algorithms provide universal solution. Depending on the data processing technique used, the best results are based on images obtained from various integration methods. It means that the method ranks do not correspond with method applicability. Methods appointed as the best ones obtain poor results in some tests and methods which came low in the rank received high rank in some tests. If this conclusion becomes confirmed, it might be necessary to revise the assessment methods of merged images.