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Abstract: Transformer moisture estimation should be conducted based on a comprehensive approach, which combines online moisture-in-oil measurement, multi-physics modeling, and theoretical derivations. This chapter presents an intelligent framework for transformer condition monitoring and diagnosis. A number of signal and data analysis techniques, which have been implemented within the proposed framework, are also described. The techniques are: digital signal processing, which is concerned with de-noising and extracting signals acquired by online sensor measurements; pattern recognition, which focuses on identifying transformer fault types; data fusion, which integrates results from different sources to determine transformer condition; and determining the health index of a transformer insulation system. A variety of case studies are provided in the chapter to demonstrate the applications of these techniques. The chapter presents a hardware and software platform for implementing the proposed intelligent transformer condition monitoring and diagnosis framework and deploying the above signal and data analysis techniques.