Color coding is a fundamental technique for mapping data to visual representations, allowing people to carry out comprehension-based tasks. Process tomography is a rapidly developing non-invasive imaging technique used in various fields of science due to its effective flow monitoring and data acquisition [KLS*19]. To study how well colormaps can support visual comprehension of tomographic data, we conduct a feasibility evaluation of 11 widely-used color schemes. We employ the same segmentation tasks characterized by Microwave Tomography (MWT) on each individual chosen colormap, and then conduct a quantitative assessment of those schemes. Based on the insight gained, we conclude that autumn, viridis, and parula colormaps yield the best segmentation results. According to our findings, we propose a colormap design guideline for practitioners and researchers in the field of process tomography.