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Mechanical excavators are widely utilized in civil/mining engineering projects. There are several types of mechanical excavators, such as an impact hammer, tunnel boring machine (TBM) and roadheader. Among these, roadheaders have some advantages (such as, initial investment cost, elimination of blast vibration, minimal ground disturbances and reduced ventilation requirements). The poor performance estimation of the roadheaders can lead to costly contractual claims. Relevance vector regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of roadheader performance. The estimation abilities offered using RVR was presented by using field data of achieved from tunnels for Istanbul’s sewerage system, Turkey. In this model, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, while net cutting rates was the output parameter. As statistical indices, coefficient of determination (R²) and mean square error (MSE) were used to evaluate the efficiency of the RVR model. According to the obtained results, it was observed that RVR model can effectively be implemented for roadheader performance prediction.