Abstract: The habit of cloud and precipitation particles is an important aspect of cloud microphysical process. And accurate information of particle shape is the premise for the calculation of many cloud microphysical parameters. At present, the airborne cloud particle imaging probe (CIP) based on the photodiode array is one of the most widely used instruments for cloud and precipitation particle shape measurement both domestically and abroad. However, the application of the information of particle shapes measured by this probe requires additional automatic particle habit identification method. In the research history of automatic recognition algorithm for cloud particle shapes, Holroyd proposed a very representative method in 1987. However, the proposed method has a serious defect in the particle habit classification, i.e., it uses the same set of threshold values to classify particle habits without considering the integrity of the particle shapes, which limits its identification accuracy. To overcome the shortcoming of the Holroyd method, an improved Holroyd cloud particle habit identification method is proposed in the present study, which uses different sets of threshold values to identify the particle shape according to whether it is a complete particle or a partial particle. Using the probe's image data from a field campaign, the accuracies of these two methods are verified. It is found that the improved algorithm can greatly improve the accuracy of the particle habit classification and its average accuracy rate can reach 80%. The improved method is then applied to airborne observation data of stratiform clouds in Taiyuan area to analyze cloud particle habit occurrence frequency, cloud particle growth mechanism, vertical distributions of ice particle number concentration and ice water content during different precipitation phases. The properties of ice crystals acquired in the stratiform clouds suggest the cloud habit classification method proposed in the present study is helpful for cloud microphysics analysis.