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This paper presents a practical review of the state-of-the-art means for applying OLM data acquisition in nuclear power plant instrumentation and control, qualifying or validating the OLM data, and then analyzing it for static and dynamic performance monitoring applications. Whereas data acquisition for static or steady-state OLM applications can require sample rates of anywhere from 1 to 10 seconds to 1 minutes per sample, for dynamic data acquisition, higher sampling frequencies are required (e.g., 100 to 1000 Hz) using a dedicated data acquisition system capable of providing isolation, anti-aliasing and removal of extraneous noise, and analog-to-digital (A/D) conversion. Qualifying the data for use with OLM algorithms can involve removing data `dead' spots (for static data) and calculating, examining, and trending amplitude probability density, variance, skewness, and kurtosis. For static OLM applications with redundant signals, trending and averaging qualification techniques are used, and for single or non-redundant signals physical and empirical modeling are used. Dynamic OLM analysis is performed in the frequency domain and/or time domain, and is based on the assumption that sensors' or transmitters' dynamic characteristics are linear and that the input noise signal (i.e., the process fluctuations) has proper spectral characteristics.