Electromyographical Signals are random in nature. The traditional signal analysis techniques such as time and frequency domain that we apply for stationary signal analysis are not providing good results of analysis. This paper aim to investigate new techniques from time-frequency domain analysis to extract prominent features for further analysis. This paper has investigated Wavelet analysis and its transforms (Discrete Wavelet transform(DWT) and Continuous Wavelet transform(CWT)). Literature survey is performed for comparison CWT and DWT analysis specifically for Electromyographical Signals. Comparison of these two transform is performed and CWT is selected over the threat of signal degradation because of down sampling (up to level 4) the signal at every level and acquired Surface Electromyographical signal has sampling frequency of 960 samples/second. CWT analysis requires large number computations at each