Alzheimer's disease (AD) is a leading cause of dementia in elderly adults. In this, the white matter (WM) tracts in brain are disintegrated leading to loss of important cognitive functionality. Recent analysis have shown that early diagnosis of AD is still a challenging task. Although several reports are available, tractography remains the most promising and clinically relevant method for in-vivo study of WM tracts. In tractography, continuous WM pathways are reconstructed from voxel based models of discrete fiber orientation generated using diffusion tensor images. In this work an attempt has been made to classify AD using average length of tracts, a significant feature extracted from tractographic brain maps. The diffusion weighted images for AD and matched controls were obtained from ADNI, an international open access repository for Alzheimer's study. Data from equal number of AD and controls were used for this study. Fiber tracking was performed for the whole brain using tract based spatial statistics algorithm. ICBM Mori Labels 1 atlas provided in the Network Analysis option of ExploreDTI was used to divide the WM into 48 anatomical regions. Classification was performed using random forest, random tree and decision stumps, and their performance indices were compared. The results show that all the classifiers are able to classify AD and controls using the extracted feature. An accuracy of 78.4% is obtained using decision stumps. Random forest and random tree provide an increased accuracy of 96% and 97% respectively. The precision and recall is also found to be higher for random forest and random tree as compared to decision stumps. These results suggest that random forest and random tree are suitable for classification of AD and controls using average tract length as a feature. In this paper, the introduction, objectives, materials and methods, results and discussions and conclusions are presented in detail. Copyright 2014, ISA All Rights Reserved.