Red Palm Weevil (RPW, Rynchophorus Ferrugineus) has become one of the most harm- ful insects for palm trees around the globe during the last two decades. The identification is difficult, because great experience and knowledge is needed for accurate identification of this insects. To automate the process of identification several attempts have been made by re- searchers to develop computer vision based algorithm to accurately identify the RPW insects. The new intelligent system is particularly useful for lay people who have no professional knowl- edge to recognize these insect species. The basic idea of the proposed study is to develop a method that can use computer vision image classification process to identify Red Palm Weevil and distinguish it from other insects like ants found in palm tree habitant. In this study, we focused on developing an algorithm that will help us to identify red palm weevil (RPW). The proposed method incorporates computer vision image classification techniques based on image enhancement and segmentation using Otsu’s algorithm. the feature extraction techniques used to classify the RPW based on color and shape and Neural Network (NN). Experimental test results for 913 dataset images, show 95 % accuracy of classification and convergence of the trained Neural Network (NN) using MATLAB simulation. GUI (Graphical User Interfacing) was developed to see real time image classification performance of the system. The system classified the RPW from other insects and gave us better classification performance.