Simulation models are used in many organisations for planning and better managing organisational systems e.g. manufacturing plant or service operations. A key part of the process of developing and using a simulation model is to experiment with the model. In order to obtain accurate measures of a model's performance care must be taken to obtain sufficient good data from the model. Particular issues are removing initialisation bias, running the model for long enough and performing sufficient replications (runs with different streams of random numbers). Decisions regarding these issues require statistical skills which many simulation modellers do not possess. As a result, many simulation models may be used poorly and incorrect conclusions reached. This research aims to develop an 'analyser' that will automatically analyse the output from a simulation model and advise the simulation modeller on an appropriate warm-up period, run-length and number of replications. In the first stage of the research existing methods for analysing simulation output will be tested to identify candidate methods for inclusion in the analyser. Candidate methods will then be adapted where necessary to make them suitable for automation. In the final stage of the research a prototype analyser will be developed and tested. The methods and analyser will be tested on example data, using real simulation models and with simulation users.