Studying the urban street network, as one of the constituent elements of urban form and the commonality of the two systems of movement and activity, plays a significant role in understanding the dynamic events in the city and solving the problems resulted from the conflicting function of the two abovementioned systems. One of the most effective structural characteristics of the street network is street network centrality which has a substantial effect on the distribution of activities and accordingly on the formation of motorized and pedestrian traffic flow throughout the city. On the other hand, one of the most important elements influencing on the network centrality is street layout. The current article aims at explaining the relationship between street layout pattern and centrality at the local scale or the microstructure of the street network. The city of Qom is an example of an old city in Iran that has an ancient urban fabric in the central core of the city and a diverse range of street layout in the middle and peripheral parts - with distinct structural features. Thus, this city is an appropriate context as the study area to explore the microstructure of the urban street network. The research process is as follows; After identifying the relatively homogeneous central zones in terms of morphology in the study area by modelling street network centrality using Multiple Centrality Assessment (MCA) method in terms of centrality index of local closeness, and applying some considered criteria, the street layout pattern of the selected zones is analyzed using several indicators of street centerline as well as blocks. Finally, the relationship between indicators of street layout pattern and the average local closeness network centrality index is explained by building a correlation matrix using Pearson’s correlation coefficient. Findings show that just 3 out of 10 selected indicators of the street layout pattern - all of which are indicators of the network centerline - have a significant correlation with average local closeness centrality index. Therefore, average local closeness centrality index has no significant correlation with block indicators. The correlation matrix shows that the higher the network lenghth as well as the proportion of three-way intersections throughout the local fabric area, the higher the average local closeness centrality index of the street network; consequently, the more centralized fabric will be at the scale of pedestrian accessibility.