Determining when and why quantitative chromatographic techniques are used in forensic science depends heavily upon the nature of the samples involved. For example, most traditional trace evidence units are more concerned with the presence ⁄absence of a material. The exact composition of the material is not an issue and a rigorous quantitative analysis is not needed, and it is neither time nor cost effective. In other cases, quantification is critical to the results of a forensic science laboratory. Examples include determining blood alcohol concentration by headspace-GC, purity determination of controlled substances by GC ⁄MS, or quantifying controlled substances and ⁄or their metabolites in bodily fluids using GC ⁄MS and ⁄or LC ⁄MS. In ‘‘Quantification in LC and GC: a practical guide to good chromatographic data,’’ edited by Hans-Joachim Kuss and Stavros Kromidas, the practical aspects of generating and analyzing chromatographic data for the purposes of quantification are discussed. At the heart of this book is that there are several contributors to the precision of a chromatographic system. These are injection, separation, detection, and integration. The focus of the book is how each of these factors (with a particular focus on the detection and integration of peaks) can manifest itself in chromatographic data. The book is organized into three parts: Part 1 (Chapters 1 – 9) is concerned with evaluating chromatographic data, integration of peaks, simulating data, deconvolution, and data interpretation. Part 2 (Chapters 10 – 13) focuses on characterizing and evaluating data from specific chromatographic techniques such as GC, LC-MS, IC, and GPC ⁄GFC ⁄SEC. Lastly, Part 3 (Chapters 14 – 17) discusses the requirements for chromatographic data analysis according to the European Pharmacopoeia, U.S. Pharmacopoeia, and the FDA. A bonus CD is also included that contains various spreadsheets and data files that are discussed in the text. Perhaps the most useful part of the book from the point of view of a forensic chemist would be Part 1. In particular, the fact that various instrument manufacturers utilize different integration algorithms is shown to have significant effects on the calculation of peak areas, and therefore sample concentrations. Furthermore, it is stressed that integration errors cannot be eliminated through the use of an internal standard. Ultimately, it is the nature of the chromatographic data itself (the number of peaks and extent to which they are resolved) that determines how best to treat the data. The default algorithms of a data system, particularly when a quantitative determination is legally relevant, should not be accepted lightly. The discussion of GC-MS in Part 2 is a good overview of the instrumentation and concepts involved, but it does not offer much that would be new to an experienced practitioner. The Chapter on LC-MS was more useful as it discusses internal standards, matrix effects, and ion suppression and how they can affect the ultimate reliability of the technique. Part 3 is less relevant to a forensic audience as it focuses on the standards used for pharmaceutical analysis. It may be interesting, however, to those who are taking steps to certify their laboratories through organizations such as ISO. Overall, the book is well organized, with key points highlighted in the text and summarized at the end of each chapter. Given that the majority of the contributing authors are from Germany, the language style and usage may be occasionally awkward for an American audience. The authors are also most concerned with laboratories that conduct environmental, clinical, or pharmaceutical analysis. However, given the increasing scrutiny of forensic science, the standards of analytical chemistry should be met in the protocols that are used routinely as a part of criminal investigations. The most relevant portions of this work, therefore, are those that apply to the treatment of chromatographic data in general (Part 1). It is this material that could be used by forensic personnel to evaluate whether their quantitative analyses are as reliable as they could be.