Water distribution system (WDS) network models are widely used by planners, water utility personnel, consultants and many others involved in analysis, design, operation or maintenance of water distribution systems. Estimation of network model parameters is a difficult but an essential step in the use of these models. Although some models have been proposed to estimate uncertainty associated with model parameters, most of these models either use complex derivative based methods such as first-order second moment (FOSM) method or use computationally intensive methods such as shuffled complex evolution metropolis (SCEM-UA). This paper presents a modified differential evolution Markov chain (DE-MC) algorithm to calibrate water distribution network models. In addition to giving both model parameters and associated uncertainty in a single run, the algorithm is simple to understand and computationally less intensive. The calibration problem was formulated as determining the network model parameters such that the best match between measured and predicted data is obtained. The proposed model was applied to calibrate two well-known examples from literature, namely, the Anytown network and the C-Town network. The network calibration parameters obtained, when input into the C-Town hydraulic model, produced reasonably good match between the predicted and the observed tank water levels and pump station flows.