The wall thickness of pulp fibers plays a major role in the paper industry, but it is currently not possible to measure this property without manual laboratory work. In 2007, researcher Ho Fan Jang patented a technique to automatically measure fiber wall thickness, combining the unique optical properties of pulp fibers with image analysis. In short, the method creates images through the use of an optical system resulting in color values which demonstrate the retardation of a particular wave length instead of the intensity. A device based on this patent has since been developed by Eurocon Analyzer. This thesis investigates the software aspects of this technique, using sample images generated by the Eurocon Analyzer prototype. The software developed in this thesis has been subdivided into three groups for independent consideration. First being the problem of solving wall thickness for colors in the images. Secondly, the image analysis process of identifying fibers and good points for measuring them. Lastly, it is investigated how statistical analysis can be applied to improve results and derive other useful properties such as fiber coarseness. With the use of this technique there are several problems which need to be overcome. One such problem is that it may be difficult to disambiguate the colors produced by fibers of different thickness. This complication may be reduced by using image analysis and statistical analysis. Another challenge can be that theoretical values often differ greatly from the observed values which makes the computational aspect of the method problematic. The results of this thesis show that the effects of these problems can be greatly reduced and that the method offers promising results. The results clearly distinguish between and show the expected characteristics of different pulp samples, but more qualitative reference measurements are needed in order to draw conclusions on the correctness of the results.