Work of breathing (WOB) offers information that may be relevant to determine the patient’s status under spontaneous mechanical ventilation in Intensive Care Unit (ICU). Nowadays, the most reliable technique to measure WOB is based on the use of invasive catheters, but the use of qualitative observations such as the level of dyspnea is preferred as a possible indicator of WOB level. In this pilot study, the activity of three respiratory muscles were recorded on healthy subjects through surface electromyography while they were under non-invasive mechanical ventilation, using restrictive and obstructive maneuvers to obtain different WOB levels. The respiratory pattern between restrictive and obstructive maneuvers was classified with the Nearest Neighbor Algorithm with a 91% accuracy and a neural network model helped classify the samples into three WOB levels with a 89% accuracy, Low: [0.3–0.8) J/L, Medium: [0.8–1.3] J/L and Elevated: (1.3–1.8] J/L, demonstrating the relationship between the respiratory muscle activity and WOB. This technique is a promising tool for the healthcare staff in the decision-making process when selecting the best ventilation settings to maintain a low WOB. This study identified a model to estimate the WOB in different ventilatory patterns, being an alternative to invasive conventional techniques