Analysis of needle electromyography signal is used for the differentiation of neuropathy and myopathy condition from the normal. Amplitude based features such as root mean square and mean absolute value are used to differentiate between normal and pathological signals. Tunable-Q wavelet transform is used to decompose the frequency bands of the signal. Further, the same set of features are used to analyse each frequency bands. The results show that the proposed approach is able to distinguish between normal and different pathological electromyography signals better than the conventional time domain analysis. It is also observed that myopathy and neuropathy signals are comprised of high frequency components than low frequency components as compared to normal signal. The proposed method yields a higher significance with a p-value <0.05 between normal and each pathological signal such as neuropathy and myopathy. © 2019 Association for Computing Machinery.