Because there is no a system based on Digital Image Processing to determine the degree of ripeness of Salak Pondoh (Salacca zalacca Gaertner Voss.) on tree, then this study has attempted to implement such a system. System was built with consists of several sub-processes. First, the segmentation process, the system will perform a search of pixels alleged pixels salak pondoh, by utilizing the features of color components r, g, b, and gray of each pixel salak pondoh then calculated large the dissimilarity ( Euclidean Distance ) against values of data features  ,  ,  ,  and   comparison. If the value of dissimilarity less than the threshold value and is also supported by the neighboring pixels from different directions has a value of dissimilarity  is less than a threshold value, the pixel is set as an object pixel, for the other condition set as background pixels. For the next, improvements through an elimination noise stage and filling in the pixels to get a perfect binary image segmentation.  Second, classification, by knowning the mean value of R and V of the entire pixel object, then the level of ripeness salak pondoh can be determined by using the method of classification backpropagation or k -Nearest Neighbor. From the test results indicate that the success of the system by 92% when using a backpropagatioan classification algorithm and 93% with k-Nearest Neighbor algorithm.