Manufacturing processes are generally monitored by observing sampled process signals. The purpose of this monitoring is to ensure process, and thereby, product consistency and to help diagnose causes of process instability. The interpretation of process signals requires the recognition of what we refer to here as primitive variations, or changes, in signal values which are typically buried in a background of other process related variations and random noise. These primitive variations include changes such as positive or negative sharp peaks, sudden step-like increases or decreases, or gradual ramp-like variations in the signals. Such changes in a given signal indicate a process change which when combined with corresponding changes in other signals could lead to the identification of the cause, or at least a rank order of possible causes, which produced these changes. In this paper, we discuss a two-level AI-based procedure for automatic recognition of these primitive changes. This procedure essentially involves applying syntactic analysis either directly to the raw process signals or, whenever not possible, to a filtered version of them. The first level, therefore, involves applying special purpose nonlinear filters which are designed to enhance or isolate, a particular primitive variation in the signal. The second level consists of a Signal Interpreter process written in LISP. This process analyses the filtered signals and produces a data structure which represents the primitive variations. A description of the entire interpretation system will be presented, and an example illustrating the application of the method to a process signal of an actual Aluminum sheet rolling mill will be shown.