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This paper describes a method of applying neural network techniques to analyze magnetic force microscopy (MFM) images of thin film media, and extract features such as vortices from the samples. The MFM images are pre-processed and applied to a self-organizing neural network (which organizes complex data into manageable groups), from which the locations and characteristics of various vortices can be observed. The results obtained illustrate that the majority of vortices present in the sample are detected (some of which are invisible to the human eye), and shows that a neural network approach is feasible to solve this type of problem. © 2002 American Institute of Physics.