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Quality control problems in which several correlated
quality characteristics are of interest are often referred to
to as multivariate quality-control problems. This subject is
particularly important today, as automatic inspection
procedures make it relatively easy to measure many
parameters on each unit of product manufactured. Various
types of multivariate quality-control charts have been
proposed to take advantage of the relationships among the
variables. In this research we propose a neural network-
based quality control procedure as an alternative means
to traditional control charts. The primary focus is on the
detection of changes in the process mean. The neural network
performance is evaluated based on average run length. An
extensive simulation study indicates that the proposed
neural network approach is better than traditional control
procedures.
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