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The aim of the presented project was to assess the efficiency of crop recognition based on microwave Advanced Synthetic Aperture Radar (ASAR) images acquired from ENVISAT-1. Investigations were conducted during two consecutive growing seasons, in 2003 and 2004. The agrometeorological conditions during the selected seasons differed markedly, which induced year-to-year variations regarding the relevant characteristics of crop canopy. Multitemporal series of ASAR alternating polarization images were used for crop differentiation. Classification was performed using a neural network classifier trained separately for each year. Field observations conducted in the western part of Poland supplied datasets for training, validation, and testing of the classifier. Despite some differences noted in the classifier performance on two datasets, the results obtained for 2003 and 2004 showed high mutual consistency.