3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac function. It provides for evaluation a more complete heart representation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes the clinician to navigate the 3D volumes to the anatomy of interest and to obtain standardized views that are similar to the 2D acquisitions. We propose an automated supervised learning method to detect standard multiplanar reformatted planes (MPRs) from a 3D echocardiographic volume. Extensive evaluations on a database of 326 volumes show performance comparable to intra-user variability and the execution time of the algorithm is about 2 seconds.