This paper describes our approaches to Na-tive Language Identification (NLI) for the NLI shared task 2013. NLI as a sub area of au-thor profiling focuses on identifying the first language of an author given a text in his sec-ond language. Researchers have reported sev-eral sets of features that have achieved rel-atively good performance in this task. The type of features used in such works are: lex-ical, syntactic and stylistic features, depen-dency parsers, psycholinguistic features and grammatical errors. In our approaches, we se-lected lexical and syntactic features based on n-grams of characters, words, Penn TreeBank (PTB) and Universal Parts Of Speech (POS) tagsets, and perplexity values of character of n-grams to build four different models. We also combine all the four models using an en-semble based approach to get the final result. We evaluated our approach over a set of 11 na-tive languages reaching 75 % accuracy. 1