Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning models to identify traffics with the few packets at the early stage.However,a basic and important problem is still unresolved,that is how many packets are most effective in early stage traffic identification.In this paper,we try to resolve this problem using experimental methods.We firstly extract the packet size of the first 2-10 packets of 3 traffic data sets.And then execute crossover identification experiments with different numbers of packets using 11 well-known machine learning classifiers.Finally,statistical tests are applied to find out which number is the best performed one.Our experimental results show that 5-7are the best packet numbers for early stage traffic identification. Accurately identifying network traffics at the early stage is very important for the application of traffic identification. Remanent years, more and more research works have tried to build effective machine learning models to identify traffics with the few packets at the early stage. However, a basic and important problem is still unresolved, that is how many many packets are most effective in early stage traffic identification. This paper, we try to resolve this problem using experimental methods. We first extract the packet size of the first 2-10 packets of 3 traffic data sets .And then execute crossover identification experiments with different numbers of packets using 11 well-known machine learning classifiers. Finally, statistical tests are applied to find out which number is the best performed one.Our experimental results show that 5-7are the best packet numbers for early stage traffic identification.