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Over the last few decades, language identification systems based on different kinds of linguistic knowledge had been studied by many researchers. Most of systems utilize one kind of linguistic knowledge only, i.e. phonotactic, phonetic repertoire, or prosody. It is possible to get the improvement by combining several linguistic knowledge. However, the combination of two systems based on different kinds of linguistic knowledge is not a trivial task. This paper presents a method where local identification results made by two individual systems, i.e. prosody-based and phonotactic-based systems, are fused in a Bayesian framework. Under this framework, local decisions, the associated false-alarm and miss probabilities are fused via Bayesian formulation to make the final decision. Experiments conducted on OGI-TS corpus demonstrate the effectiveness of this decision-level fusion strategy.