In modern manufacturing processes, large quantities of multivariate measurement data are routinely available through automated in-process sensing. The data generally contain a great deal of buried diagnostic information that can aid in identifying and eliminating major root causes of manufacturing variation. Traditional statistical process control (SPC) tools, which were designed for situations in which data are much less abundant, do not take full advantage of in-process measurement. In this talk I will discuss an approach that is much better suited for this scenario. The approach has origins in the sensor-array signal processing arena, where it is commonly referred to as blind source separation. The talk will cover basic blind source separation principals and the analogy between diagnosing multiple sources of manufacturing variation and separating multiple radar (for example) source signals from an antennae array. I will also discuss extending blind separation methods to be more effective and versatile in manufacturing. One advantage of the overall approach is that it lends itself well to graphical visualization methods for helping process operators and engineers understand the major root causes of manufacturing variation.