Premature ventricular contraction(PVC) is the most frequent arrhythmia encountered in clinical practice. PVC may occur in health subjects, which is not imminently life-threatening but may require therapies to prevent further problems. So,the timely PVC recognition becomes very important for the analysis of electrocardiogram(ECG), especially for the remote ECG monitoring using mobile phones. In this paper,a construction method of personalized ECG template and a PVC recognition method based on template matching were studied. Firstly, we selected 43 ECG recordings from the MIT-BIH arrhythmia database. All recordings were divided into two datasets(DS1for training and DS2 for testing) and each dataset approximately contained the same proportion of PVC beats. Subsequently, for each recording(30 min) in DS1, the first5 min recordings were used to construct the personalized ECG template and the last25 min recordings were used for the R-wave peaks detection and PVC recognition,where the template matching method were used. The validity of the proposed methods was tested using DS2. The results showed that: 1) high beat detection accuracy was achieved for both PVC beats and non-PVC beats; 2) the sensitivity and specificity of PVC recognition were 99.11% and 99.96% for the first 5 min recordings respectively,99.17% and 99.43% for the last 25 min recordings respectively. All the proposed methods can be real-time performed, which show a promising prospect for the application of ECG mobile phones. PVC may occur in health subjects, which is not imminently life-threatening but may require therapies to prevent further problems. So, the timely PVC recognition becomes very important for the analysis of electrocardiogram (ECG), especially for the remote ECG monitoring using mobile phones. In this paper, a construction method of personalized ECG template and a PVC recognition method based on template matching were studied. First, we selected 43 ECG recordings from the MIT -BiH arrhythmia database. All recordings were divided into two datasets (DS1 for training and DS2 for testing) and each dataset contained contained (30 min) in DS1, the first 5 min recordings were used to construct the personalized ECG template and the last 25 min recordings were used for the R-wave peaks detection and PVC recognition, where the templat The validity of the proposed methods was tested using DS2. The results showed that: 1) high beat detection accuracy was achieved for both PVC beats and non-PVC beats; 2) the sensitivity and specificity of PVC recognition were 99.11% and 99.96% for the first 5 min recordings respectively, 99.17% and 99.43% for the last 25 min recordings respectively. All the proposed methods can be real-time performed, which show a promising prospect for the application of ECG mobile phones.