In this thesis we primarily consider the development and applications of risk-sensitive identification schemes for fast convergence of parameter estimates. Initially we consider the risk-sensitive identification problem for hidden Markov models (HMMs). Finite dimensional filters are derived, for the risk-sensitive identification of HMMs, by combining the techniques of maximum likelihood (ML) id...