Rapidly evolving sensor technologies, which employ advanced techniques, such as lasers, machine vision, and pattern recognition, have the potential to greatly improve quality control activities in the finished product inspection and process monitoring. In this paper, a neural network model was developed to probe the dependence between the quality of finished product and sensor measurements which were collected to monitor the failure (sudden fracture) of a tool in the manufacturing process. A real case in mass production is employed to illustrate the modeling procedure. Utilizing the trained neural network, the quality information of finished product can be further obtained from the online tooling sensor measurements. The result reveals that the tooling sensor measurements not only can be employed to detect the process condition (wear out or sudden fracture) but also can provide valuable information to monitor the quality performance of finished product simultaneously.