Knowledge of accurate parameter estimates is essential for incorporating normal tissue complication probability (NTCP) models into biologically based treatment planning. The purpose of this work is to derive parameter estimates for the Lyman-Kutcher-Burman (LKB) NTCP model using a combined analysis of multi-institutional toxicity data for the lung (radiation pneumonitis) and parotid gland (xerostomia). A series of published clinical datasets describing dose response for radiation pneumonitis (RP) and xerostomia were identified for this analysis. The data support the notion of large volume effect for the lung and parotid gland with the estimates of the n parameter being close to unity. Assuming that n = 1, the m and TD(50) parameters of the LKB model were estimated by the maximum likelihood method from plots of complication rate as a function of mean organ dose. Ninety five percent confidence intervals for parameter estimates were obtained by the profile likelihood method. If daily fractions other than 2 Gy had been used in a published report, mean organ doses were converted to 2 Gy/fraction-equivalent doses using the linear-quadratic (LQ) formula with alpha/beta = 3 Gy. The following parameter estimates were obtained for the endpoint of symptomatic RP when the lung is considered a paired organ: m = 0.41 (95% CI 0.38, 0.45) and TD(50) = 29.9 Gy (95% CI 28.2, 31.8). When RP incidence was evaluated as a function of dose to the ipsilateral lung rather than total lung, estimates were m = 0.35 (95% CI 0.29, 0.43) and TD(50) = 37.6 Gy (95% CI 34.6, 41.4). For xerostomia expressed as reduction in stimulated salivary flow below 25% within six months after radiotherapy, the following values were obtained: m = 0.53 (95% CI 0.45, 0.65) and TD(50) = 31.4 Gy (95% CI 29.1, 34.0). Although a large number of parameter estimates for different NTCP models and critical structures exist and continue to appear in the literature, it is hard to justify the use of any single parameter set obtained at a selected institution for the purposes of biologically based treatment planning. Our expectation is that the proposed model parameters based on cumulative experience at various institutions are more representative of the overall practice of radiation therapy than any single-institution data, and could be more readily incorporated into clinical use.