Lung nodules are tiny lumps of tissue that are common in the lungs. The nodule may be benign or malignant; malignant nodules are cancerous and can grow rapidly. For a long time, X-ray images of the chest have been utilized to diagnose lung cancer. We developed in this paper a computer aid diagnosis system (CAD) to atomically classify a set of lung x-ray images into normal and abnormal (with nodule and no-nodule) cases. We used 180 images in this work, the images are in full size no filtering or segmenting process were applied, 75 of them are for normal cases and the other 105 are for abnormal cases, at the same time 120 of the images have been used to train the classifier and 60 for testing. Our classifiers were fed with a variety of features, including LBP (local binary pattern) and statistical features. And a classifier was able to identify cases with nodule from cases without nodule with an accuracy (ACC) of 86.7%