Surface electromyography is often used in the control of some devices, especially in the control of the prostheses. In this work, surface electromyography (EMG) is used to perform the function of a drone's remote control joystick. To design a wearable joystick and select the best classification algorithm, the system first learns thumb finger movements when moving a joystick forward, backward, right, left, and neutral, and then classifies new thumb movements as learned by different classifiers. The data set was obtained with our own EMG device. Autoregressive (AR) modelling, mean absolute deviation, waveform length, entropy, integrated absolute amplitude, mode, percentile and interquartile are used as the feature extraction. Various classification algorithms such as neural networks (NN), discriminant analysis (DA), k-nearest neighbour (KNN), support vector machine (SVM) and Naive Bayes have been used and compared. The performance of each classifier algorithm is defined as the ratio of correctly classified samples to the total number of samples. According to experimental results, LDA gives the highest correct classification ratio and KNN is the most robust classifier.