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In this paper we propose a robust and fully automated lumbar herniation diagnosis system based on clinical MRI data which will not only aid a radiologist to make a decision with increased confidence, but will also reduce the time needed to analyze each case. Our method is based on three steps: 1) We automatically label the five lumbar intervertebral discs in a sagittal MRI slice using a probabilistic model and then extract an ROI for each disc using an Active Shape Model. 2) We generate relevant intensity and texture features from each disc ROI. 3) We construct five different classifiers (SVM, PCA+LDA, PCA+Naive Bayes, PCA+QDA, PCA+SVM) and combine them in a majority voting scheme. We perform 5-fold cross-validation experiments and achieve an accuracy of 94.85%, specificity of 95.9% and sensitivity of 92.45% for 35 clinical cases, i.e. a total of 175 lumbar intervertebral discs.