Pulse wave is an important physiological signal widely used in clinic. In practical applications, the pulse wave recordings are easily corrupted by different interferences. Sometimes, it is very difficult to eliminate the noise by commonly used filtering methods. In this study, we proposed a filtering method based on the characteristics of pulse wave recordings to remove the noisy outliers. Firstly, five characteristics, short-term energy (SE), ascending intensity difference (AID), descending intensity difference (DID), ascending time difference (ATD), and descending time difference (DTD), were chosen as metrics and calculated from cardiac pulse wave. Then the median lines of the five metrics were obtained using a median filter, respectively. An acceptable value range around the median line of each metric was set based on histogram distribution analysis and was used to examine pulse wave recordings cardiac-cycle-by-cycle. For each cardiac cycle, when one or more of its five characteristic values exceed(s) the acceptable range, the pulse wave recording segment was discarded from further analysis. With this proposed method, the noisy outliers could be efficiently identified from the pulse wave recordings. This suggests that the proposed preprocessing method would be useful in improving the assessment performance of pulse-wave-based clinical applications. Additionally, the method might also be extended used in other physiological signals pre-processing, such as ECG, blood pressure wave, etc.