While labeled as a new version of the highly successful 1988 first edition [1], this edition is more a new printing than a reworking. Those who are already acquainted with the previous edition and feel comfortable with FORTRAN should not buy this new one, since for them nothing new has been provided. The more C inclined will discover that the FORTRAN code can be nicely translated into C. Unfortunately, the C code is not part of the book and is only on the disk. While it is not strictly necessary that a copy of the source code eat up pages of the book, discussion of data structures and programming techniques interrelated with the particular application area of the programming language is always welcome. This review is oriented toward people who do not know the previous edition and who learn, teach, or practice digital signal processing (DSP) algorithms. The authors' approach to discussing DSP algorithms is reader friendly for practitioners. Although complex concepts are covered, such as spectral estimation and phase unwrapping, the theoretical background is concise and clear and the accompanying FORTRAN code is explained well and is usually short enough. Chapter 1 gives a brief introduction to DSP. Chapter 2 introduces signals, especially signals that will be dealt with further, namely sampled data and the usual Z transform on them. The real thing begins in chapter 3, on discrete Fourier transform routines. No lengthy discussions on Fourier transform internals are to be found here; that theme belongs to other types of books. Here the authors concentrate on algorithm usage, and from that point of view, although it is short, this is not a chapter to skip. The fourth chapter introduces random number routines (which are useful as noise generators), and uses software modules from previous chapters to introduce and implement spectral analysis (the Welch periodogram approach), cross spectra, and coherence functions. Chapter 5 describes routines for computing the frequency response and time response of linear systems. The system has to be presented as input to these routines in terms of its Z transform function coefficients. Direct, cascade, parallel, and even lattice forms are supported. The next two chapters introduce IIR filters. Chapter 6 is devoted to their implementation, while coefficients computing is approached in chapter 7. The previously mentioned forms, now in the IIR filter context, are discussed in chapter 6, along with a small section on FIR filters. In the next chapter, most classical design methods are presented (with their implementations), including Butterworth and Chebyshev. Chapter 8 gives routines for designing FIR filters by the windowing method. Chapter 10 addresses the important topic of multirate processing. While this technique is mostly useful in a real-time context, this presentation fits well even for educational purposes. The last four chapters are more mathematically involved than the previous ones. Chapter 11, on least-squares design and modeling, is in fact an introduction to parametric spectrum estimation and modeling. The classical Levinson-Durbin algorithm is at the heart of the chapter. Chapter 12 presents adaptive filtering routines in some detail. Discussion is centered on the LMS algorithm, and sample applications are presented. Curve fitting routines for waveform characterization as well as some important parameter computations, such as the rise and fall time of signals, are the theme of chapter 13. Finally, chapter 14 focuses on some less commonly performed signal processing operations. These operations are data windowing, which is useful in Fourier spectrum estimation; simple phase unwrapping, as used in …