Automatic detection of necrosis in histological images is an interesting problem of digital pathology that needs to be addressed. Determination of presence and extent of necrosis can provide useful information for disease diagnosis and prognosis, and the detected necrotic regions can also be excluded before analyzing the remaining living tissue. This paper describes a novel appearance-based method to detect tumor necrosis in histopathogical whole slide images. Studies are performed on heterogeneous microscopic images of gastric cancer containing tissue regions with variation in malignancy level and stain intensity. Textural image features are extracted from image patches to efficiently represent necrotic appearance in the tissue and machine learning is performed using support vector machines followed by discriminative thresholding for our complex datasets. The classification results are quantitatively evaluated for different image patch sizes using two cross validation approaches namely three-fold and leave one out cross validation, and the best average cross validation rate of 85.31% is achieved for the most suitable patch size. Therefore, the proposed method is a promising tool to detect necrosis in heterogeneous whole slide images, showing its robustness to varying visual appearances.