Abstract File type identification (FTI) has become a major discipline for anti-virus developers, firewall designers and for forensic cybercrime investigators. Over the past few years, research has seen the introduction of several classifiers and features. One of these advances is the so-called n-grams analysis, which is an interpretation of statistical counting in classified fragments. Recently, n-grams based approaches were already successfully combined with computational intelligence classifiers. However, the academic body of literature is scant when it comes to a comprehensive explanation of machine learning based approaches such as neural networks (NN) or support vector machines (SVM). For example, how the input parameters, including learning rate, different values of n for n-grams, etc. influence the results. In addition, very few studies have compared the scalability of NN vs. SVM approaches. Therefore, a systematic research in comparing different approaches is needed to address these questions. Hence, this paper investigates this type of comparison, by focusing on the n-gram analysis as a feature for the two different classifiers: SVMs and NNs. This paper details our experiments with two NNs and four SVMs, using linear kernels and RBF kernels on RealDC datasets. In general, we found that SVM-based approaches performed better than the NN, but their scalability is still a challenge.