Structural vector autoregressive models are usually identified by combining covariance restrictions implied by the data and identifying restrictions suggested, for example, by economic theory. This paper proposes an approach to assess, in a Bayesian sense, whether or not the data support a candidate set of identifying restrictions. I analyse sign restrictions. The researcher represents her uncertainty regarding the validity of the restrictions using a uniform prior that covers the parameter space both where the restrictions are satisfied and where they are not satisfied. The correlation pattern in the data then guides whether the probability mass in favour of the candidate restrictions increases or not from prior to posterior. To outline the key mechanism I use two examples, a two-equation model of demand and supply and a simplified New Keynesian model.