Ancient palm leaf manuscripts in Thailand have been preserved by many organizations for the protection and retrieval of traditional knowledge. With advanced computer technology, digitized media is now commonly used to record these documents. One objective of such work is to develop an efficient image processing system that could be used to retrieve knowledge and information automatically from these manuscripts. Binarization is an important stage during preprocessing of the manuscripts for subsequent extraction of text and characters. The output is then used for further processes such as character recognition and knowledge extraction. However, there is no single binarization technique that is suitable for all documents. This study aims to improve the binarization process intelligently by applying machine learning techniques to classify the optimal selection of binarization techniques of palm leaf manuscripts. Experiments results are reported and this technique could be applied for automatic selection of binarization techniques in other image processing problems.