Captive reptile environments promote parasite proliferation, leading to infections and weak immune responses. Furthermore, the manual analysis of stool samples for parasite detection is laborious and susceptible to errors. Therefore, this paper proposes a Bag-of-Visual Word approach to classify six captive reptile parasitic agents, namely Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides and Taenia, and identify the absence of such parasites from microscope stool images. Based on relevant key points and the SURF descriptor, the Bag-of-Visual Word representation is integrated with a support vector machine-based classifier. We trained the model on a publicly available dataset composed of 3616 images labeled by experts. Experimental results show competitive performance (84.4% accuracy) compared to computationally intensive methods such as conventional convolutional neural networks.