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In order to detect the ripe tomato in unstructured environment for robotic harvesting, a tomato recognition algorithm using noncolor coding approach was developed. The proposed algorithm was consist of offline training and online recognition. In the process of offline training, a strong classifier was obtained using AdaBoost algorithm with Haar-like features. The Haar-like feature is a kind of noncolor coding feature which can be extracted by integral figure calculation. In the online recognition process, the tomato object was detected by using the strong classifier which was obtained in the offline training process. Two couples of comparative tests were conducted to study the influence of the types of Haar-like features and training times on the performance of the proposed algorithm. The results showed that the Cstyle Haar-like features and 20000 training times were the optimal parameters for the size of training set. The results of online recognition tests indicated that about 93.3% ripe tomatoes existing in the testing samples set were successfully detected. The proposed tomato recognition approach was also successfully applied in the unstructured environment with various disturbances such as occluded, overlapping, and varying illumination, which indicated that the proposed tomato recognition algorithm was selfadaptive and robust. It was available to be applied in the vision recognition system for a harvesting robot.