Background Disparities in health outcomes between the poor and the rich are increasingly attracting attention from researchers and policy-makers. However, policies aimed at reducing inequities need to be based on a sound assessment of the nature, magnitude and determinants of the problem, as policy decisions based on intuition are likely to be misguided. Objective The work investigates the relationship between household socio-economic status and under-five mortality at Rufiji DSS in year 2005. The specific objectives were; 1.To construct wealth and concentration indices for households with children under age five. 2. To measure health inequality by poorest / least poor mortality rate ratio and the use of concentration index 3. To determine significance in gradient of mortality rates across wealth index quintiles by a trend test (chi-square) 4. To assess the magnitude of association between socio-economic status of households and under-five mortality. Methods Data from Rufiji DSS, Tanzania was used for the analysis. Out of 11,189 children under five years of age from 7298 households, 251 died in the year 2005. These yielded a total of 9341.6 PYO in 2005 which was used in the analysis. Household wealth index was constructed by use of Principal Component Analysis (PCA), as a proxy measure of each household SES. From this index households were categorized into five quintiles (i.e., poorest, poorer, poor, less poor and least poor). Kaplan-Meier (K-M) survival estimates of incidence rates were used to estimate mortality rates per 1000 PYO for infants (0-1), children (1-4) and under-fives. Health inequality was measured by poorest to least poor mortality rate ratio and by computing mortality concentration indices. Trend test chi-square was used to determine significance in gradient of mortality rates across wealth index quintiles. Risk factors of child mortality were assessed by the use of Poisson regression taking into account potential confounders. Results The result indicates that the mortality rate was higher for infants (123.4 per 1000 PYO, 95% CI (104.3, 146.1)) than for children aged 1-4 years (17.3 per 1000 PYO, 95% CI (14.3, 20.9)). Under-five mortality was 26.9 per 1000 PYO (95% CI (23.7, 30.4)). The poorest to least poor ratio were 1.5, 3.8 and 2.4 for infants, children, and under-five year olds, respectively indicating that children in the poorest quintile were more likely to die as compared to those in the least poor household. Computed values for concentration indices were negative (infant C= -0.07, children C= -0.24 and under-five C= -0.16) indicating a disproportionate concentration of under-five mortality among the poor. The mortality rates trend test chi-square across wealth index quintiles were significant for both children (P<0.001) and under-five year old children (P<0.001) but not for infants (P=0.10). In univariate Poisson regression, children in the least poor households were shown to have a 58% significantly reduced risk of dying as compared to the poorest households [crude RR=0.42, P < 0.001, 95% CI (0.27 - 0.62)]. The effect of household socio-economic status attenuated after adjusting for maternal education, maternal age and occupation. Children in vi the least poor households had a 52% significantly reduced risk of dying as compared to the poorest households [adjusted RR=0.48, P = 0.002, 95% CI (0.30 - 0.80)]. Conclusion The study shows that household socio-economic inequality is associated with under-five mortality in Rufiji DSS in 2005 and that the survival advantage of under-five year old children is associated with maternal education. Reducing poverty and making essential health services more available to the poor are critical to improving overall childhood mortality in rural Tanzania.