The spread of the pulmonary disease among humans is a very rapid process and it stands as the third highest killer in the United States of America. Computed Tomography (CT) scanning allows us to obtain detailed images of the pulmonary anatomy including the airways. The complexity of the tree makes the process of manual segmentation tedious, time-consuming, and variant across individuals. The resultant airway segmentation, whether arrived at manually or through the aid of computers, can then be used to measure airway geometry, study airway reactivity, and guide surgical interventions. The thesis addresses these problems and suggests a fully automated technique for segmenting the airway tree in three-dimensional (3-D) micro-CT images of the thorax of an ex-vivo mouse. This novel technique is a several step approach consisting of: 1. The feature calculation of individual voxels of the micro-CT image, 2. Selection of the best features for classification (obtained from 1), 3. KNN-classification of voxels by the best selected features (from 2) and 4. Region growing segmentation of the KNN classified probability image. KNN classification algorithm has been used for the classification of the voxels of the image (into airway and non-airway voxels) based on the image features, the results of which have then been processed using the region growing segmentation algorithm to obtain the final set of results for segmentation. The segmented airway of the ex-vivo mouse lung volume can be analyzed using a commercial software package to obtain the measurements.