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Bone Histomorphometry is an important analysis in preventing and treatment of cancer and osteoporosis patients, providing quantitative information about the bone structure. X-Ray Micro-Computer Tomography is a non-invasive and non-destructive imaging technique, with a high space resolution that enables magnified images. In the histomorphometric analysis of such images, it is possible to use filters and binarization, nevertheless these techniques may cause loss of information. In this paper we describe the usage of Artificial Neural Networks (ANNs) in Microtomography X-Ray imaging bone recognition as a part of a histomorphometric analysis research with raw images obtained at the Synchrotron Radiation for Medical Physics (SYRMEP) beamline of the ELETTRA Laboratory at Trieste, Italy. A Multilayer Perceptron Model for the ANNs with Error Back-Propagation and supervised learning has been used in the recognition task. The classification of bone subimages yielded a Receiver Operating Characteristic Curve with an area under curve of 1.000, which means that the ANN is able to distinguish successfully the bone mass. The images obtained are also depicted herein. The quality and characteristics of the X-Ray Computer Microtomography are compatible with the ANN-based proposed methodology, avoiding the loss of information due to image manipulation.