TOPICS CIMSA inherits part of the legacy of a series of meeting started in 1996 to present and discuss recent developments, results, and practical experiences in soft computing technologies in support of measurement systems and applications, especially in the areas of industrial cases. The wide and increasing needs of adaptable and flexible solutions for many industrial, environmental, and engineering applications point out the importance of using design methodologies and implementation technologies with high ability of adaptation and evolution. Soft computing is one of the most relevant answers to such needs: neural networks, fuzzy logic, and genetic/evolutionary algorithms are fundamental keys to tackle these difficult problems. On the other hand, accuracy and uncertainty issues as well as suited data acquisition systems must be carefully considered in these applications since the quality of the solution greatly relies on them. Up to now, analysis and experiments have been performed by scientists and practitioners mainly to understand the underlying technologies and methodologies, but without any specific focus on the mandatory need of a quantitative assessment and a metrological analysis. Measurement science and technologies are in fact vital to ensure the correct and effective use of soft computing technologies in real environments. CIMSA is directed to fill this gap in knowledge and practice, especially by focusing on the quantitative aspect of measurement issues for industrial, environmental, and engineering applications. Typical topics are concerned with all aspects of soft computing technologies related to measurement systems and the related applications, from the points of view of both theory and practice. This includes but is not limited to: intelligent measurement systems; accuracy and precision of neural and fuzzy components; intelligent sensor fusion; intelligent monitoring and control systems; neural and fuzzy technologies for identification, prediction, and control of complex dynamic systems; evolutionary monitoring and control; neural and fuzzy signal/image processing for industrial and environmental applications; image understanding and recognition; soft-computing technologies for robotics and vision; soft computing technologies for medical and bioengineering applications; hybrid systems; fuzzy and neural components for embedded systems; neural and fuzzy implementations for measurement systems; neural, fuzzy and genetic/evolutionary algorithms for system optimization and calibration; neural and fuzzy diagnosis of components and systems; reliability of fuzzy and neural components; fault tolerance and testing in fuzzy and neural components; neural and fuzzy techniques for quality measurement. The interactive format of the conference allows for in depth discussion and confrontation among attendees. Each brief formal presentation will be followed by a longer informal plenary discussion that can be expected to address broadly the specific approaches and results presented by the authors, the rationale underlying the particular methodologies employed, the experimental and theoretical approaches, the practical difficulties, any unanswered questions, key insights and lessons learned, and the possibility of extension to other problems of interest to the participants.