The purpose of this paper is to classify between healthy and sick chicken based on their dropping. Most chicken farm management system in Malaysia is highly dependent on human surveillance method. This method, however, does not focus on early disease detection hence, unable to and alert chicken farmers to take necessary action. Therefore, the need to improve the biosecurity of chicken poultry production is essential to prevent infectious disease such as avian influenza. The classification of sick and healthy chicken based solely on chicken's excrement using the support vector machine is proposed. First, the texture is examined using grey-level co-occurrence matrix (GLCM) approach. A GLCM based texture feature set is derived and used as input for the SVM classifier. Comparison are made using more and then less extracted features, less extracted features and also applying Gabor filter to these features to see the effect it has on classification accuracy. Results show that having more features extracted using GLCM techniques allows for greater classification accuracy.