Two main problems concerned with faulty systems with high risk, are complexity and improvement of the behavior of the systems. For instance, in aviation control systems or in chemical and nuclear plants, the behavior of the system is very important whenever a fault occurs or a part of system malfunctions. In such systems, if the control part of the system doesn’t detect the fault in time and doesn’t reconfigure itself suitably, it’s human operators must hurt and the important information and components can be lost. Consequently, interest of researchers has grown significantly in the field of fault detection, isolation and reconfiguration. Due to regulatory concerns in recent financial crises, financial intermediaries’ credit risk assessment is an area of renewed interest in the business community. In this research, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly. our new fuzzy support vector machine treats every sample as both positive and negative dir=ltr Support vector machine is based on vapnik statistical learning theory. it is one of the best algorithms for fault detection and isolation in dynamic systems. Signature authentication of applications of a support vector machine. The purpose of signature verification is distinguish genuine signatures from forgeries. Extended regression gives a better criterion in comparison with dynamic time warping and euclidean distance for signatures similarity. Using all point matching for equalizing signal length decrease differ genuine signatures from forgeries. In this research, a technique to differential between genuine and forgery signature, based on extremum matching for equalizing signal length is proposed. In addition, in three tank benchmark fault is detected by using support vector machine. Also, it is shown than support vector machine has better performance than radial base function and multi layer perceptron neural networks in this application. Furthermore, influence of roll parameter in linear and nonlinear support vector machine is discussed in fault detection and dir=ltr Key words: support vector machine, statistical learning theory, dynamic time warping, extrimum matching