In many chemical engineering processes the input control parameters and the product output quality are monitored dynamically in real-time. Almost all the processing steps associated with semiconductor manufacturing are chemical processes in which the surface of crystals or thin films are being chemically modified. In situ monitoring of the wafer attributes in real-time is essentially nonexistent in modern semiconductor manufacturing. For plasma etching processes several new diagnostic techniques (e.g., full-wafer imaging interferometry and ellipsometry) provide improved endpoint observation and some provide metrics for the state of the wafer at a given time. However, the methods that do provide metrics are usually quite expensive for a manufacturing environment. We propose a method whereby simple and economic endpoint methods can indicate in real-time the state of the wafer. Our method consists of finding the algorithm to map in situ wafer-state signatures (e.g., interferometry, ellipsometry) to wafer attributes and then mapping the process signatures (e.g., reflected rf power, pressure, flow rate, OES) to wafer-state signatures. From these we then have an abstract mapping from the process signatures to the wafer attributes in real-time. In this article we suggest that a learning machine can perform the mapping between process signatures, as a function of time and wafer state signatures, as a function of time.