Recent advances in computers and networking technologies and a fast growing internet community have created immense distributed databases located miles away that have the ability to be updated continuously without the knowledge of the possible and prospective users. The ability to collect and store all kinds of data has outpaced the capabilities of an individual to analyze, summarize, and extract "knowledge" from the data. Traditional methods of data analysis, based mainly on the analysts dealing directly with the data, are no longer the best alternative to be used. Although the database technology provided the basic tools for efficient storage and lookup for large data sets, the issues of how to enable engineers to understand large bodies of data remains a difficult problem. Recently, data mining approaches based on artificial neural networks, fuzzy logic, machine learning, statistics, expert systems, and data visualization have created new intelligent tools for automated data mining and knowledge discovery. All these changes will have a profound impact on current practices used in manufacturing. The way that bills of materials are created, products are designed, and process plans are generated, will definitely be different with the availability of this new technology. In this paper the nature of these changes and their implications on product design and manufacturing are discussed and the basic issues regarding the design of Smart Data Mining Systems are examined with examples.