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Decision rules for interpreting control chart data
The Western Electric rules are decision rules in statistical process control for detecting out of control or non random conditions on control charts. Locations of the observations relative to the control chart control limits (typically at ±3 standard deviations) and centerline indicate whether the process in question should be investigated for assignable causes. The Western Electric rules were codified by a specially appointed committee of the manufacturing division of the Western Electric Company and appeared in the first edition of a 1956 handbook, that became a standard text of the field. Their purpose was to ensure that line workers and engineers interpret control charts in a uniform way.

An important aspect of the Western Electric rules are the zone rules, designed to detect process instability, and the presence of assignable causes. Data sets of observations are appraised by four basic rules, that categorize the occurrence of data samples in a set of zones defined by multiples of the standard deviation. DescriptionChart exampleRule 1Any single data point falls outside the 3σ limit from the centerline (i. e., any point that falls outside Zone A, beyond either the upper or lower control limit)Rule 2Two out of three consecutive points fall beyond the 2σ limit (in zone A or beyond), on the same side of the centerlineRule 3Four out of five consecutive points fall beyond the 1σ limit (in zone B or beyond), on the same side of the centerlineRule 4Eight consecutive points fall on the same side of the centerline (in zone C or beyond)

These rules are evaluated for one side of the center line (one half of the control band) at a time (e. g., first the centerline to the upper control limit, then the centerline to the lower control limit). Data satisfying any of these conditions as indicated by the control chart provide the justification for investigating the process to discover whether assignable causes are present and can be removed. Note that there is always a possibility of false positives: Assuming observations are normally distributed, one expects Rule 1 to be triggered by chance one out of every 370 observations on average. The false alarm rate rises to one out of every 91.75 observations when evaluating all four rules.