Musculoskeletal disorders (MSDs) caused by muscle fatigue have been a major problem for industry which needs to be resolved to save costs related to human resource development (extra training and compensation). Detailed fatigue monitoring researches aimed at finding the best fatigue indices is not new although studies on the causes of fatigue can be explored further. Identification analysis is required to monitor the factors that influence muscle performance characteristic of surface electromyography (sEMG) signal. Periodogram monitoring technique applies a frequency domain signal and represents the distribution of the signal power over the frequency. It is a technique that allows the tracing of small changes in the behaviour of sEMG signal when external parameters are varied. This technique is used in this paper to monitor the sEMG signal changes in muscle performance when the lifting height and load mass are varied. The periodogram amplitude, which represents the power, increases with the rise in lifting height and load mass. From the frequency representation of the periodogram, the root mean square voltage (Vrms) is calculated where the muscle performance characteristic could be further identified. The Vrms also shows a similar trend when the lifting height and load mass are varied proving the periodogram technique is useful to monitor changes in the muscle performance during manual lifting.