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Spectral analysis is a powerful tool to investigate stellar properties and ithas been widely used for decades now. However, the methods considered toperform this kind of analysis are mostly based on iteration among a fewdiagnostic lines to determine the stellar parameters. While these methods areoften simple and fast, they can lead to errors and large uncertainties due tothe required assumptions.  Here we present a method based on Bayesian statistics to find simultaneouslythe best combination of effective temperature, surface gravity, projectedrotational velocity, and microturbulence velocity, using all the availablespectral lines. Different tests are discussed to demonstrate the strength ofour method, which we apply to 54 mid-resolution spectra of field and cluster Bstars obtained at the Observatoire du Mont-M\'egantic. We compare our resultswith those found in the literature. Differences are seen which are wellexplained by the different methods used. We conclude that the B-starmicroturbulence velocities are often underestimated. We also confirm the trendthat B stars in clusters are on average faster rotators than field B stars.

Bayesian Statistics as a New Tool for Spectral Analysis: I. Application for the Determination of Basic Parameters of Massive Stars