Breast cancer has become a rapidly prevailing disease among women all over the world. In term of mortality, it is considered to be the second leading cause of death. Death risk can be reduced by early stage detection, followed by a suitable treatment procedure. Contemporary literature shows that mammographic imaging is widely used for premature discovery of breast cancer. In this paper, we propose an efficient Computer Aided Diagnostic (CAD) system for the detection of breast cancer using mammography images. The CAD system extracts largely discriminating features on the global level for representation of target categories in two sets: all 20 extracted features and top 7 ranked features among them. Texture characteristics using cooccurrence matrices are calculated via the single offset vector. Multilayer perceptron neural network with optimized architecture is fed with individual feature sets and results are produced. Data division corresponds as 60%, 20%, and 20% is used for training, cross-validation, and test purposes, respectively. Robust results are achieved and presented after rotating the data up to five times, which shows higher than 99% accuracy for both target categories, and hence outperform the existing solutions.