You will get a row in the .CSV per iteration of HB. You'll want to decide which iterations to "throw away" because convergence wasn't achieved. Typically, researchers throw away the first 5000 or first 10000. Then, you only pay attention to the remaining iterations.
You'll find the columns labeled in the .csv file, so you can see which columns refer to what. You'll first have the alpha estimates (intercept) when the covariates are zero, and you'll have the columns referring to the adjustments to the alpha values according to the coded covariates.
It's easier to interpret the intercept alphas if you have specified continuous covariates that are zero-centered.
A typical procedure is to count for how many of the rows a particular covariate beta is consistently either >0 or <0. For example, if your covariate were income, you will see for a particular part-worth utility the beta (either positive or negative) for each iteration. If across 98% of the used iterations (after convergence is assumed) the income covariate is associated with a >0 beta, then you are 98% confident that income has a positive effect upon preference for that part-worth utility (for the sample).