The purpose of this study was to examine the relationship between child/maternal factors (child's functional status, level of technology dependence, mother's depressive symptoms, length of caregiving duration, amount of home health care nursing hours, race, family income and age of the child) and (a) family functioning, as well as (b) normalization in families with a child who is technology dependent. Additionally, this study examined whether there are differences in family functioning, normalization and mother's depressive symptoms based upon the child's level of technology dependence (mechanical ventilation, intravenous nutrition/medication, respiratory/nutritional support). A descriptive, correlational design was used in this cross-sectional study. Data were collected using the Functional Status II-Revised, Center for Epidemiological Studies- Depression, Feetham Family Functioning Survey and a subscale of the Normalization Scale in face-to-face interviews. Mothers of 103 children who are technology dependent and living at home comprised the sample. The sample of mothers aged 21-66 years were 73% Caucasian and 27% women of color, who cared for their technology dependent child aged 7 months-16 years; 73% received solely respiratory/nutritional support. Pearson correlations revealed that greater depressive symptoms and less use of normalization were significantly associated with poorer family functioning. In the hierarchical regression analysis, 35% of the variance in family functioning was explained, primarily by level of depressive symptoms. Conversely, several independent variables/covariates were found to be significant predictors and explained 34% of the variance in normalization. Better child's functional status, less depressive symptoms, fewer hours of nursing care, older child and Non-Caucasian race or Hispanic ethnicity were related to greater normalization efforts. Statistical analyses for mediation reveal that a mother's depressive symptoms are a mediator between the child's functional status and normalization. ANOVA analysis showed no statistically significant differences in outcomes based upon the child's level of technology dependence. Mothers of children who are technology dependent are at high risk for psychological distress that can affect overall family functioning. This work will be pivotal in designing interventions to assist families in the home management of this vulnerable and growing population of children.