Palm oil is one of the largest and significant contributions to the Malaysian economy. As such, makes it important to improve the quality of this product since defects on palm oil fruit may affect the palm oil production. Bruise is one of the defects on palm oil as it is unavoidable during the field material activities. This can increase the number of Free Fatty Acid (FFA) and reduces the palm oil quality produced. The present study proposes an application to identify the bruises on palm oil fruit using image processing approach. This is performed by combining four texture features extracted from Grey Level Co-occurrence Matrix that are contrast, correlation, energy and homogeneity and six shape features which are area, perimeter, major axis length, minor axis length, eccentricity and equidiameter to identify four different bruise stages that are major, minor, moderate and no bruise. The bruise classification is conducted using Multi-Class Support Vector Machine (SVM). The present research manages to achieve 80.38% classification accuracy based on the dataset employed.