This study presents a new method to exploit both accent and grouping structures of music in meter estimation. The system starts by extracting autocorrelation-based features that characterize accent periodicities. Based on the local boundary detection model, we construct grouping features that serve as additional cues for inferring meter. After the feature extraction, a multi-layer cascaded classifier based on neural network is incorporated to derive the most likely meter of input melody. Experiments on 7351 folk melodies in MIDI files indicate that the proposed system achieves an accuracy of 95.76% for classification into nine categories of meters.