Muscle fatigue is a neuromuscular condition which causes a decline in muscle performance. Surface electromyography (sEMG) signals are widely used to evaluate muscle fatigue and these signals are highly complex in nature. To address this, advanced signal processing techniques are necessary. In this work, an attempt has been made to analyze the complexity of sEMG signals associated with fatigue conditions using Multiscale Approximate Entropy (MSApEn) technique. Signals are recorded from biceps brachii muscles of fifty healthy subjects while performing curl exercise and it is divided into six equal segments to avoid variability in endurance time. The first and last segments are considered as nonfatigue and fatigue conditions respectively. The signals are preprocessed and MSApEn is evaluated. Further, four features namely median (MED), variance (VAR), high scale sum (HSS) and low scale sum (LSS) are extracted from each segment. The results indicate a distinct variation in the MSApEn values. It is found that the signals are complex in both fatigue and nonfatigue conditions. In addition, features namely the MED, HSS and LSS are found to be low in fatigue case. The t-test performed on these features shows high statistical significance (p-value<0.005). It appears that this method can be used to analyze the complexity of sEMG signals in varied clinical conditions.