There is an increasing need to deploy speech recognition systems supporting multiple languages/dialects on portable devices worldwide. A common approach uses a collection of individual monolingual speech recognition systems as a solution. However, such an approach is not practical for handheld devices such as cell phones due to stringent restrictions on memory and computational resources. In this paper, we present a simple and effective method to develop multilingual acoustic models that achieve comparable performance relative to monolingual acoustic models but with only a fraction of the storage space of the combined monolingual acoustic model set.