The objectives of this study are to assess the level and differentials of child labor and to findrnout the main socioeconomic and demographic determinants of child labor in SNNP Region.rnThe source of data used in this study was the 1999 National Labor Force Survey conductedrnby the Central Statistical Authority (CSA).rnThe data were analyzed using descriptive and inferential techniques. Frequency distribution,rnthat is, uni-variate analysis was used to see the percentage share among the backgroundrnvariables. Both the bi-variate test and multi-variate statistical models were also employed tornsee the association of the independent variable with the dependent and to find out therndeterminants and differentials of child labor among different socioeconomic andrndemographic variables.rnThe bi-variate technique using the chi-square test showed a strang associarion between thernbackground variable and the dependent variable. The findings of the multi-variate logisticrnregression revealed that males were highly exposed to child labor as compared to theirrnfemale counter parts and children aged J 0-14 years are significantly exposed to child laborrnthan in the age group 5-9. Furthermore, those children who were not auending school arernmuch more expose to child labor than who were attending. Living in the rural areas sholvedrna relatively higher risk for child labor than living in the urban areas. With respect to thernmigration status of head children with migrant head has a lower risk than with non-migrantrnhead Furthermore, households headed by Never Married members offhe household and byrnfemales greatly push children into child labor. A child who is non-relative to the head isrnhighly exposed to child labor. In addition to this children who lost one of their parems andrnthose who lost both of them are more likely to be a child labor compared to the referencerncategory. Household size is also directly related to child labor. It was also found that therneducational status of the household head is inversely related to child labor. Employmentrnstatus and occupation of the head also became significant. These results were also justifiedrnby employing a separate logistic regression model for demographic and socioeconomicrnvariables and almost simili)J' results were obtained.rnFinally, recommendations for policy measures that should be taken by the government andrnthe society at large were suggested to alleviate child laborer in the Region.