Identification of motion intention and muscle activation strategy is necessary to control human–machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve patternrecognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it. The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the ‘‘isolated masses’’ method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation.