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Most statistical process control (SPC) methods are not suitable for monitoring nonlinear and state-dependent processes. This article introduces the context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two-context at any monitoring period of time. The first is a reference tree that represents the "in control" reference behavior of the process; the second is a monitored tree, generated periodically from a simple of sequenced observations, that represents the behavior of the process at that period. The Kullback-Leibler (KL) statistic is used to measure the relative "distance" between these two trees, and an analytic distribution of this statistic is derived. Monitoring the KL statistic ample of buffer-level monitoring in a production system demonstrates the viability of the new method with respect to conventional methods. [ABSTRACT FROM AUTHOR]