EMG data analysis forms a crucial part of clinical diagnosis in patients suffering from movement disorders. Any movement involves a synchronized use of agonist and antagonist muscles. In patients suffering from movement disorders, this coordination between different groups of muscles can be a problem. EMG data is used to study muscle activity; and the measurements are significantly noisy. To study a complex movement, one needs to derive useful models from the EMG data (for different muscle groups). In this paper we present a detailed analysis of surface EMG signals from the perspective of linear segmentation, non-linear and chaotic approaches. Comparisons of the linear regression models of surface EMG suggest the ARIMA model to be the best. A chaotic analysis of surface emg signal to determine the maximum lyaopunov exponent, the embedding dimension and time lag has been discussed in the later part of the paper. The EMG signal is found to follow high dimensional chaotic dynamics from the positive value of the Maximum Lyapunov Exponent.