Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image using spatial information from the high-resolution panchromatic image, while preserving the spectral information in the multispectral image. Various pansharpening algorithms are available in the literature, and some have been incorporated in commercial remote sensing software packages such as ERDAS Imagine® and ENVI®. The demand for high spatial and spectral resolutions imagery in applications like change analysis, environmental monitoring, cartography, and geology is increasing rapidly. Pansharpening is used extensively to generate images with high spatial and spectral resolution. The suitability of these images for various applications depends on the spectral and spatial quality of the pansharpened images. Hence, the evaluation of the spectral and spatial quality of the pansharpened images using objective quality metrics is a necessity. In this work, quantitative metrics for evaluating the quality of pansharpened images are presented. A performance comparison, using the intensity-hue-saturation (IHS)-based sharpening, Brovey sharpening, principal component analysis (PCA)-based sharpening, and a wavelet-based sharpening method, is made to better quantify their accuracies.