The aim of this study is to develop a color visualization system for multispectral images in the near-infrared region (NIR, 800–1000 nm). Samples with the same color or appearance in the visible region, which are therefore indistinguishable to the human eye, can have different reflectance or transmittance spectra in other parts of the electromagnetic spectrum, specifically in the NIR. Therefore, these samples can be differentiated by taking into account this extra information. In this work, we use a multispectral system that we have developed recently in order to obtain five images of several samples with varying spectral reflectance, corresponding to different spectral bands. We then define a color space representation which associates the camera responses to the color channels of a calibrated CRT monitor. Therefore, a pseudo-colored image is obtained. Several possible associations are presented, some of them based on methods which attempt to imitate human color vision but in the NIR region, and others for maximizing colorimetric discrimination between the objects, based on principal component analysis (PCA). Finally, the color differences between samples are evaluated using several parameters. The methods which provide the best results in terms of visual discrimination are based on PCA analysis, but the methods related to human color vision keep information of the NIR spectral reflectance of the samples.