Part numbers are widely used within an enterprise throughout the manufacturing process. The point of entry of such part numbers into this process is normally via a Bill of Materials, or BOM, sent by a contact manufacturer or supplier. Each line of the BOM provides information about one part such as the supplier part number, the BOM receiver's corresponding internal part number, an unstructured textual part description, the supplier name, etc. However, in a substantial number of cases, the BOM receiver's internal part number is absent. Hence, before this part can be incorporated into the receiver's manufacturing process, it has to be mapped to an internal part (of the BOM receiver) based on the information of the part in the BOM. Historically, this mapping process has been done manually which is a highly time-consuming, labor intensive and error-prone process. This paper describes a system for automating the mapping of BOM part numbers. The system uses a two step modeling and mapping approach. First, the system uses historical BOM data, receiver's part specifications data and receiver's part taxonomic data along with domain knowledge to automatically learn classification models for mapping a given BOM part description to successively lower levels of the receiver's part taxonomy to reduce the set of potential internal parts to which the BOM part could map to. Then, information about various part parameters is extracted from the BOM part description and compared to the specifications data of the potential internal parts to choose the final mapped internal part. Mappings done by the system are very accurate, and the system is currently being deployed within IBM for mapping BOMs received by the corporate procurement/manufacturing divisions.