After lung cancer, breast cancer is known to be the greatest cause for death among females . The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network (ANN) method named self organizing maps (SOM) with a supervised classifier called stochastic gradient descent (SGD). Also a comparative analysis is performed between the proposed approach and three supervised state of the art machine learning techniques decision tree (DTs), random forests (RF) and support vector machine (SVM). Initially SGD method is used in isolation for the classification task and then it is made to perform the classification after being hybridized with the unsupervised ANN technique on Wisconsin Breast Cancer Database (WBCD) . The comparison is based up on classification accuracy that is produced by generating a confusion matrix. For verifying consistency of accuracy values, the classification task was repeated with Internet Advertisements Dataset . The results of the classification experimentation using hybridization of SOM with SGD are much more superior to SGD in isolation. All the accuracy values have been computed after achieving a ten-fold cross validation on the both the datasets to further verify the classifier's performance. © 2015 IEEE.