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A CT-scan is a vital tool for the diagnosis of lung cancer via tumor detection. Developing a classifier to make use of the information in CT-scan images could provide a non-invasive alternative to histopathological techniques such as needle biopsy to identify tumor types. Image features extracted from 74 lung tumor objects of CT-scan images are used in classifying tumor types. Classification is done into two major classes of non-small cell lung tumors, Adenocarcinoma and Squamous-cell Carcinoma, each constituting 30% of all lung tumors. In this first of its kind investigation, a large group of 2D and 3D image features which were hypothesized to be useful are evaluated for effectiveness in classifying the tumors. Classifiers including decision trees and support vector machines are used along with feature selection techniques (Wrappers and Relief-F) to build models for tumor classification. Results show that over the large feature space for both 2D and 3D features it is possible to recognize tumor classes with about 68% accuracy, showing new features may be of help. The accuracy achieved using 2D and 3D features is similar with 3D easier to use.