PIP: The development of small cities and towns (SCTs) in China has been rapid. Factor analysis was used to analyze the development of 124 SCTs with a population 5000 in Liaoning Province. The focus was on the dimension, level of development, and classification criteria. The purpose, principles, and method of factor analysis are described. Based on data from the Survey of Economic Development of SCTs in Liaoning Province, 17 per capita indexes were selected as index variables. Only the first 3 factors had eigenvalues 1 (4.3, 2.3, and 1.4) and an accumulated variance of .8. 80% of the information for the 17 index variables was explained by these 3 factors and 8 indexes was higher and designated the industry commerce factor. The eigenvalue of 4.3 means that industry and transportation are major factors in SCT development in Liaoning. The second factor was correlated highly with 6 indexes and was designated the basic facility factor. This means that transportation facilities, education, and health care are important components of SCT development. The third factor was correlated with 3 indexes and was higher than the second factor. It was designated the agricultural production factor. The correlation between these 3 factors was insignificant. Computational coefficients can be derived for these factors. Comparisons were made between towns on the value of the factor. Towns were grouped into the following categories: economic/cultural center towns, ethnic minority towns, remote mountainous towns, small harbor towns, communications hub towns, market towns with a population 10,000, industrial mining towns, small scale hydroelectric base towns, and science-based towns. This classification was based on the direction of SCT development. When the values of each factor were converted into ordered values of high or low, 6 types of SCTs were apparent: 1) developed-type with high values for all 3 aspects (17 towns or 13.7% of total), 2) developing-type with low values (30 towns with 24.2%), 3) urban-type with high industry/commerce and facilities and low agricultural production (33 towns with 26.6%), 4) rural-type with high values for agricultural production (34 towns and 27.4%), 5) satellite-type with industry and agriculture high and facilities low (5 with 4%), and 6) administrative-type with facilities high and agriculture and industry low (5 and 4%).