The aim of this study is to detect masses in mammograms based on textural features. Suspicious regions are identified following the bilateral image subtraction of left and right breast image pairs. The study uses the nipple as a common rotational point thereby facilitating an alignment with the highest correlation before subtraction. Within this study, 144 breast images from the MIAS database are considered. Five co-occurrence matrices are constructed at four different distances for each suspicious region. Twelve texture features defined by Haralick et. al. [5], angular second moment, correlation, contrast, entropy, inverse difference moment, sum average, sum entropy, sum variance, difference entropy, difference variance and two information measures of correlation. Two further features defined by Chan et. al [2], inertia and difference average, are also computed giving fourteen texture measures. Following classification of six principal components calculated for the extracted features using an ANN (Artificial Neural Network) and 10-fold cross-validation, an average recognition rate of 77% was achieved. Using Receiver Operating Characteristic (ROC) analysis, the overall sensitivity of the technique measured by the value of Az, was found to be 0.74.