Stochastic Petri Nets are a modelling formalism that can be conveniently used for the analysis of complex models of Discrete Event Dynami Systems (DEDS) and for their performance and reliability evaluation. The automatic construction of the probabilistic models that underly the dynamic behaviours of these nets rely on a set of results that derive from the theory of untimed Petri nets. The paper introduces the basic motivations for modelling DEDS and briefly overviews the basic results of net theory that are useful for the definition of Stochastic Petri Nets and Generalized Stochastic Petri Nets. The different approaches that have been used for introducing the concept of time in these models are discussed in order to provide the basis for the definition of SPNs and GSPNs as well. Details on the solution techniques and on their computational aspects are provided. A brief overview of more advanced material is included at the end of the paper to highlight the state of the art in this field and to give pointers to relevant results published in the literature.