Wrist motion pattern recognition is significant in various applications, such as human-computer interaction and rehabilitation. This paper presents a study on wrist motion pattern recognition using electromyography (EMG) signal processing techniques in conjunction with machine learning. We constructed a dataset, capturing time and frequency characteristics associated with three distinct wrist movements: extension, flexion, and relaxation. The main goal of this project was to develop accurate and reliable models to classify wrist motion patterns from EMG signals. Several machine learning algorithms were used, including random forest and neural networks. We also used principal component analysis (PCA) to optimize feature selection and enhance classification performance. The results demonstrated promising outcomes for the random forest and neural network classifiers. The random forest classifier achieved an accuracy of 75%. In contrast, the neural network, specifically a multilayer neural network, achieved an accuracy of 90%. Including PCA for feature selection significantly contributed to the overall performance improvement in both classifiers. This study’s findings show the potential for practical applications in human-computer interaction systems and rehabilitation technologies.