Several years of war have created a humanitarian crisis in the Democratic Republic of Congo (DRC) with extensive disruption of civil society, the economy and provision of basic services including health care. These challenges are faced against a background of heavy disease burden, especially of HIV/ AIDS, tuberculosis, diarrhoea, acute respiratory infection, and malaria. Health policy and planning in the DRC are constrained by a lack of reliable and accessible population data.Thus there is currently a need for primary research to guide programme and policy development for reconstruction and to measure attainment of the Millennium Development Goals (MDGs).This study uses the 2001 Multiple Indicators Cluster Survey to disentangle children's health inequalities by mapping the impact of geographical distribution of childhood morbidity stemming from diarrhoea, acute respiratory infection, and fever. We account for important risk factors and nonlinear effects using a Bayesian geo-additive regression model based on Markov Chain Monte Carlo techniques.We observe a low prevalence of childhood diarrhoea, acute respiratory infection and fever in the western provinces (Kinshasa, Bas-Congo and Bandundu), and a relatively higher prevalence in the south-eastern provinces (Sud-Kivu and Katanga). However, each disease has a distinct geographical pattern of variation. Among covariate factors, child age had a significant association with disease prevalence. The risk of the three ailments increased in the first 8-10 months after birth, with a gradual improvement thereafter. The effects of socioeconomic factors vary according to the disease.Accounting for the effects of the geographical location, our analysis was able to explain a significant share of the pronounced residual geographical effects. Using large scale household survey data, we have produced for the first time spatial residual maps in the DRC and in so doing we have undertaken a comprehensive analysis of geographical variation at province level of childhood diarrhoea, acute respiratory infection, and fever prevalence. Understanding these complex relationships through disease prevalence maps can facilitate design of targeted intervention programs for reconstruction and achievement of the MDGs.