The northern fringe of sub-Saharan Africa is a region that is considered to be particularly vulnerable to climate variability and change, and it is a location in which food security remains a major challenge. To address these issues, it is essential to develop global data sets of the geographic distribution of agricultural land use. The objectives of this study were to test an original data mining approach for classifying and mapping the cropped land in West Africa using coarse-resolution imagery and to compare the classification results with those obtained from a classic ISODATA approach. The data mining approach is able to handle large volumes of data and is based on different descriptors (65) of the land use, including the spatial and temporal satellite-derived metrics of 12 MODIS NDVI 16-day composite images and the static attributes taken from field surveys. The classic ISODATA method showed that 68.3% of pixels from a SPOT reference map were correctly classified in three validation sites versus 57.8% for the data mining approach. Validation by field observations showed equivalent results for both methods with an F-score of 0.72. The results of this study demonstrated the relevance of the use of data-mining tools for large-area monitoring.