Abstract: This study investigated how to classify plant species with compound leaf features using machine-learning approaches. Many traditional classification systems used a single feature, such as geometry or texture. Although such systems can achieve good results in small databases, with increasing records, the similarity in single features between different species will be remarkable, thus reducing the accuracy in large databases. This study examined how to extract compound features and proposes a novel preprocessing method and new ways to extract width and edge information, which are more detailed than with most of the state-of-the-art approaches. The compound features can reduce the influence of the over-fitting problem, so the algorithm can be used for larger databases. We examined up to 21 kinds of plants (extracting width, color, edge and texture data) and 1 915 digital images and achieved an accuracy of 93%. Finally, we analyzed the effect of each feature on classification results. We could reduce the feature’s dimension to about 100 without losing much classification accuracy.