Dynamic fluctuations of nuclear plant sensors contain information about their response characteristics and bandwidth features. The random fluctuations can be characterized by using auto-regression (AR) time-seriesmodels. These discrete-time models are then utilized to estimate time-domain and frequency-domain signatures. Prior to developing these models, the sensor measurements are enhanced by filtering both low-frequency and high-frequency components using wavelet transforms. The use of wavelet transform for signal conditioning results in minimum distortion of the signal bandwidth, and thus provides an effective approach for data pre-processing. This integrated approach is applied to plant data from a pressurized water reactor (PWR). Univariate AR models were established for several pressure transmitter data, and used to estimate response time parameters of sensors and their frequency spectra. The results of this integrated approach demonstrate the improvement in the sensor signature estimation compared to the direct use of plant measurements.