The most damaging events controlling the low cycle fatigue life of a mechanical component are typically characterized by a relatively few, large amplitude, low frequency loadings where, for this paper, signals below 60 to 100Hz are considered low frequency. These are usually accompanied by a large number of small amplitude, high frequency loadings that may contribute little or no damage and often are merely a measure of system noise. Over sampling by factors of five to ten relative to the sampling theorem is often recommended to achieve the time domain resolution necessary for an accurate capture of peaks and valleys used in component fatigue evaluation. This over sampling limits the elapsed time capacity of digital recording devices used for field tests, slows data transfer rates, and expands database storage requirements.The authors propose the application of sampling rate interpolation to allow for the effective decimation of data while at the same time achieving high time domain resolution of the damaging events. To demonstrate the approach, a rosette time history will be analyzed at different sampling rates, showing how histogram statistics and ultimately fatigue life predictions are preserved. An approach, based on frequency domain energy considerations, will be proposed for the selection of sampling rates.