The aim of this research was to design, implement and calibrate a computer vision system (CVS), for use in real-time, in order to measurement of color on minimally processed yacon slices. For this purpose a device (software and hardware) was designed and implemented which consisted of two steps: a) image acquisition and b) image processing and analysis. Both, the algorithm and the graphical interface (GUI), were developed in Matlab. The CVS calibration was performed using a conventional colorimeter (Model CIEL* a* b*). Minimally processed yacon slices were developed and stored at 5 °C. Then, color changes were estimated every 2 hours, obtaining its color parameters (L*, a*, b* and C*, H*, ΔE*). L* decreased from 65.9 to 60.8, with a tendency to black color; a* increased from 7.3 to 17.7, approaching the red color; b* increased from 35.1 to 41.5, presenting a tendency to yellow color. Moreover, C* increased from 35.9 to 40.1, H* decreased from 78.2 to 66.9 and AE* increased from 2.6 to 13.2; these changes are mainly due to enzymatic browning of yacon slices. Were estimated the errors of the color parameters: eL* = 5.001%, and ea* = 2.287%, and eb* = 4.314 %, which ensure adequate and efficient automation application in industrial processes in the quality control in the food industry sector.