Radiation therapy dose distributions to eradicate tumor cells are typically constrained in extent or intensity to minimize the risk of injury to nearby critical normal tissues. With the widespread use of 3D image-based treatment planning systems, the question naturally arises how patient-specific anatomy and treatment differences affect outcome. It has long been known, that for many organs, variations in the fractional volume irradiated to high doses greatly alters the dose to achieve a given complication level (the "isoeffective dose") [1]. Smaller irradiated fractional volumes often lead to a much lower risk of complication; this is often referred to as the "volume-effect" in the literature, but would be more correctly referred to as the "dose-volume" effect. Normal tissue complication probability (NTCP) modeling is simply the ongoing effort to understand the risk of normal tissue injury as a function of the 3D dose distribution. Recently, there has been a steady accumulation of NTCP studies [2], and this is expected to continue or even accelerate in the future. NTCP models are particularly needed when the "volume-effect" becomes important (i.e., injury depends on the detailed dose distribution), such as for skin, lung, or liver. In this chapter we will review the basic principles of NTCP modeling, as well as publications related to selected endpoints (xerostomia, radiation pneumonitis, late rectal toxicity), and several issues related to the use of NTCP models, especially relating to their safe use. Other recent reviews which further discuss data on endpoints of interest in treatment planning include Deasy and Fowler [3], Moiseenko et al. [4], and the slightly older but still invaluable Seminars in Radiation Oncology issue, edited by Randy Ten Haken, devoted to dose-volume effects in normal tissues [2]. A useful review of models and model principles are the chapters by Jackson and Yorke [5], and Yorke [6]. Many technical issues in modeling dose-volume outcomes were also discussed by Deasy et al. [7].This chapter describes standard NTCP models as well as our own approach, which is more data-driven and image-based [8]. This contrasts with the more common approach of assuming the validity of a specific model and then attempting to fit the model parameters to a given data set. The term "image-based" indicates