Anomalies in an equipment plasma are diagnosed by monitoring transient data filtered through a fast Fourier transform (FFT). The anomalies were experimentally simulated with variations in process factors, including source power, pressure, and Ar flow rate. A real-time match network monitoring system was used to collect radio-frequency match data and match positions were utilized as signatures representing certain changes in plasma states. The FFT position provided better diagnostic sensitivity than the statistically characterized one, which was more apparent when factor effects on steady plasma were less significant.