Fresh water is a fundamental resource for environmental and social processes, essential for the emergence and development of life. Mapping and monitoring surface water is therefore of great importance for understanding hydrological processes and managing water resources. This study was conducted in the largest mountain range in Mexico, the Sierra Madre Occidental (SMO), spanning the states of Chihuahua, Sonora, Sinaloa, Durango, Nayarit, Zacatecas and Jalisco. The SMO has an area of 251,648 km2 and elevations ranging from 300 m to 3,347 m. Due to its size, orography and geographical location, this region which constitutes the main source of water for northern Mexico, contains a wide variety of ecosystems, which in turn promote high species diversity. The objectives of this study were: 1) to detect water bodies in the SMO using Sentinel-2 satellite images with high spatial resolution, and 2) to make an inventory of water bodies in the SMO by vegetation type. In this study, 120 Sentinel-2 satellite images were used. The satellite has a multispectral sensor with a spatial resolution of 10 m. An atmospheric correction was carried out for each image using the dark object subtraction method. The normalized difference water index (NDWI) was used to detect and delimit water bodies. Before the validation process, the water bodies that had been detected were cross-tabulated against the polygons of the different vegetation types. These vegetation types were classified as follows: forest class, which includes pine, oak, pine-oak, oak-pine and cloud forest; tropical forest class, which includes low and medium deciduous tropical forest; forest with secondary herbaceous and shrubby vegetation class; scrub class; grassland class and chaparral class. The polygons were obtained from the INEGI 1:250,000 vectorial Series VI data on land use and vegetation. The number of water bodies (and their area) detected in each vegetation class were obtained through geoprocessing using the ArGIS 10.7 program. Estimates of the areas of the water bodies were validated by estimating the kappa index, and by means of confusion and error matrices. These were used to calculate the areas of the water bodies and their confidence intervals for each vegetation class. A total of 26,394 water bodies were detected. The vegetation type with the most water bodies was forest, with 46.86%, followed by grasslands, with 21.47%. The water bodies detected had areas ranging from 43 m2 to 64 km2. Pixel values from the NDWI associated with water bodies ranged from 0.1 to 0.8. The median was close to 0.3, and the quartiles were 0.2 and 0.4. The kappa index values indicated good and excellent agreement for the precision of water body detection in the different vegetation types. The lowest value, K = 0.62, was associated with pine-oak and cloud forest vegetation types. This was due to shadows that were mistaken for water bodies (251 shadows). The highest kappa index values, K = 0.91, were obtained for grasslands, where very few shadows (13 shadows) were confused with water bodies. The overall precision was 0.738, and the error matrix showed that the class with the most errors of commission was grassland, with a user accuracy value of 0.227. The class that had the most errors of omission was scrub, with a producer accuracy value of 0.351. This study makes a substantial contribution to the 800 water bodies previously reported for the SMO in the Series VI data for land use and vegetation from 2016.