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Muscle fatigue is considered one of the main causes of sEMG changes during repetitive contractions performed for long periods of time. In the current work we are proposing and evaluating an approach in order to reduce the effects of muscle fatigue on upper limb myoelectric control using adaptive LDA. A dataset of surface EMG signals from nine subjects, including six normally-limbed and three upper limb amputees, was processed. The EMG signal was encoded using four time-domain features and four coefficients of an auto-regressive model. Adaptive and non-adaptive strategies were compared using Accuracy, False Positive Rate, Sensitivity and F1 score. Results obtained with normally-limbed subjects show that in normal scenario while muscle fatigue increases, the recognition accuracy and Sensitivity of the classifier decrease from more than 90 % to less than 58 %; False Positive Rate increases from around 9 % to 36.2 %, and F1-score decreases from 0.9 to 0.6. In contrast, parameters maintain a more stable and higher performance when adaptive LDA is evaluated. Although control in amputees shows a reduction in performance compared with normally-limbed subjects, results show a similar trend.  The Wilcoxon sum rank test shows a significant increase in performance of upper limb myoelectric control tasks when adaptive LDA is used. The main limitation of this work is the need of supervision in the adaptation procedure to decide if a trial is adequate for retrain the model, making the proposed method difficult to implement in a real scenario. Future work is needed in order to obtain a parameter that serves to choose the proper trial for model retraining.