In this work, an attempt has been made to demarcate Tuberculosis (TB) sputum smear positive and negative images using statistical method based on Gray Level Co-occurrence Matrix (GLCM). The sputum smear images (N=100) recorded under standard image acquisition protocol are considered for this work. Haralick descriptor based statistical features are calculated from the sputum smear images. The most relevant features are ranked by principal component analysis. It is observed that the first five principal components contribute more than 96% of the variance for the chosen significant features. These features are further utilized to demarcate the positive from negative smear images using Support Vector Machines (SVM) and Differential Evolution based Extreme Learning Machines (DE-ELM). Results demonstrate that DE-ELM performs better than SVM in terms of performance estimators such as sensitivity, specificity and accuracy. It is also observed that the generalization learning capacity of DE-ELM is better in terms of number of hidden neurons utilized than the number of support vectors used by SVM. Thus it appears that this method could be useful for mass discrimination of positive and negative TB sputum smear images.