DNA-Microarray data from 5 different batches of bovine skeletal muscle samples were analyzed (146 samples). After preprocessing, expression data from animals treated with corticosteroids and controls from the different batches (89 samples) were used to train a Support Vector Machines (SVMs) classifier. The optimal number of gene probes chosen by our classification framework was 73. The SVMs with linear kernel built on these 73 biomarker genes was predicted to perform on novel samples with a high classification accuracy (Matthew's correlation coefficient equal to 0.77) and an average percentage of false positive and false negative equal to 5% and 6%, respectively.