Bovine mastitis is considered the main disease of dairy herds in the world, causing the greatest economic losses to the dairy sector. In Brazil, production losses caused by mastitis are estimated between 12 and 15%, accounting to more than three billion liters of milk per year. In addition, mastitis poses risks to public health, due to the possibility of contamination of milk with antibiotic residues or with microorganisms and their toxins. The high frequency of mastitis pathogens resistant to tetracycline, erythromycin, gentamicin, ampicillin and penicillin, commonly marketed in Brazil, has been proven in a previous project conducted by Embrapa Dairy Cattle and its partners, evidencing the importance of monitoring bacterial resistance to antimicrobials in the Brazilian cattle herds. Thus, the main objective of this project is to develop, standardize and validate a multiplex PCR-based method to detect, directly in bovine milk samples, the main genes involved in bacterial resistance to the antimicrobials mentioned above. It is expected that this technology will allow detecting, through specific amplification, at least six of the most prevalent antimicrobial resistance genes in the primary pathogens of bovine mastitis, i.e. Streptococcus agalactiae and Staphylococcus aureus. After its validation, this methodology can be used to monitor the resistance to the main antimicrobials marketed in the Brazilian market for the treatment of bovine mastitis. Besides, it will be possible to reduce the time required for the evaluation of antimicrobial resistance of about one week (as currently performed using bacterial isolation and antibiograms) to approximately 8 to 9 hours. It is also expected that the new method will show superior sensitivity and ease of scaling as compared to the conventional one. This methodology may also be used in the detection of resistance genes in isolated bacteria. Thus, this tool will also be of great importance for the expansion of knowledge concerning the genetic basis and mechanisms of transmission of antimicrobial resistance of bacteria that cause mastitis. The large amount of data that can be generated with the use of this tool in a short period of time may guide future work focusing on antimicrobial rejuvenation via nanotechnology and other actions to control the disease in specific microregions.