An image analysis algorithm for the classification of cherries in real time, by processing its digitalized color images, was developed, and tested. A set of five digitalized images of color pattern, corresponding to five color classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the fruit colored area. Histogram analysis was carried out for the RGB and HSV color spaces, wherein Red and Hue components showed differences between each of the specified color patterns, of the exporting reference system. This information allowed developing a hybrid Bayesian classification algorithm, based on the components R and H, and testing its accuracy with a set of cherry samples, within the color range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm it showed 100% of effectiveness in classifying a sample set of cherries, pertaining to the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, which permits an affordable commercial deployment.