For many decades automatic facial expression recognition has scientifically been considered a real challenging problem in the fields of pattern recognition or robotic vision. The current research aims at proposing Relevance Vector Machines (RVM) as a novel classification technique for the recognition of facial expressions in static images. The aspects related to the use of Support Vector Machines are also presented. The data for testing were selected from the Cohn-Kanade Facial Expression Database. We report 90.84% recognition rates for RVM for six universal expressions based on a range of experiments. Some discussions on the comparison of different classification methods are included.