Because they are exploited in many fields such as medicine, agriculture, chemistry and others, plants are of fundamental importance to life on earth. Before it can be used, a plant need to firstly be identified and categorized. However, a manual identification task requires time, and it is not an easy task to do. This is because some plants look visually similar to the human eye, whereas some others may be unknown to it. Therefore, there has been an increasing interest in developing a system that automatically fulfils such tasks fast and accurate. In this paper, we propose an automatic plant classification system based on a parallel combination technique of multiple classifiers. We have considered using three widely known classifiers namely Naïve Bayes (NB), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). Our system has been evaluated using the well-known Flavia dataset. It has shown a better performance than those obtained using only one classifier.