Upper limb elbow movement in terms of flexion-relaxation is a complex physical phenomenon in human daily life, particularly during synchronized activities, such as exercising, reaching, pointing, and manipulating. Improper elbow movement might cause musculoskeletal injury, fatigue, pain, or disorders in the upper limb muscles. This study aimed to identify subject-specific electromyographic (EMG) signal patterns based on changes in five elbow joint angles (at 0°, 30°, 60°, 90° and 120°) during maximum (100%) voluntary isometric (static) contraction. Surface electromyographic (sEMG) signals were recorded from the upper arm biceps brachii muscle using a three-channel wearable sensor. A non-parametric machine learning algorithm called k-nearest neighbors (k-NN) was used to build a model that can determine the EMG characteristics and thus discriminate between elbow joint angles. Fifteen time domain features were extracted from the recorded EMG signal and those were used for classification purposes. Two cross validation (CV) methods, namely, leave-one-out (LOO) and k-fold, were used to examine and validate the model. The results showed that k-fold CV showed higher mean classification accuracies (89.68%) than the LOO method (82.49%). Our classification-based results from sEMG signals acquired with five elbow joint angles could aid the development of more advanced rehabilitation assistive devices and further improve the neuromuscular activities of the upper arms. Additionally, this result showed that wearable technology has potential application for remotely monitoring and controlling motor rehabilitation exercises.