An assessment of electrocardiogram (ECG) signal quality has become an unavoidable first step in most holter and ambulatory ECG signal analysis applications. In this paper, we present a simple method for automatically detection and classification of ECG noises. The proposed method consists of four major steps: moving average filter, blocking, feature extraction, and multistage decision-tree algorithm. In the proposed method, the dynamic amplitude range and autocorrelation maximum peak features are extracted for each block. In the first decision stage, a amplitude-dependent decision rule is used for detecting the presence of low-frequency (LF) noise (including, baseline wander (BW) and abrupt change (ABC) artifacts) and the high-frequency (HF) noise (including, power line interference (PLI) and muscle artifacts). In the second decision stage, the proposed method further classifies the LF noise into a BW noise or a ABC noise using the local dynamic amplitude range feature. The HF noise is classified into a PLI noise or a muscle noise using the local autocorrelation maximum peak feature. The proposed detection and classification method is tested and validated using a wide variety of clean and noisy ECG signals. Results show that the method can achieve an average sensitivity (Se) of 97.88%, positive productivity (+P) of 91.18% and accuracy of 89.06%. © 2015 IEEE.