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Ethiopia is one of the developing countries in the world, and its economy depends on both agriculture and industry. From agriculture products, maize crop is one of the most important for both human beings as well as for animals in Ethiopia. Even though, maize has a great share and nutritive value, it is very susceptible to different types of diseases. Currently, there are more than 72 maize diseases reported in Ethiopia caused by fungi, bacteria, nematode and viruses. Among this diseases maize leaf blight, maize common rust, and leaf spot are common diseases that attack maize leaf. There are different traditional mechanisms to recognize and classify maize leaf diseases by chemical analysis or visual observation. However, the traditional mechanisms to recognize maize leaf diseases have their own draw backs, such as expensiveness, inconsistent, prone to error, take more time, require professional staff, specialized instruments, inefficient etc. Therefore, we are motivated to develop maize leaf diseases recognition and classification model using imaging and machine learning techniques in order to support experts.