Signature-based anti-viruses are very accurate, but are limited in detecting new malicious code. Dozens of new malicious codes are created every day, and the rate is expected to increase in coming years. To extend the generalization to detect unknown malicious code, heuristic methods are used; however, these are not successful enough. Recently, classification algorithms were used successfully for the detection of unknown malicious code. We earlier investigated the optimized conditions in which highest-level accuracy is achieved, in terms of the percentage of malicious files. In this paper we describe the methodology of detection of malicious code based on static analysis and a chronological evaluation, in which a classifier is trained on files till year k and tested on the following years. The evaluation was performed in two setups, in which the percentage of the malicious files in the training set was 50% or 16%. Using 16% malicious files in the training set showed a clear trend, in which the performance improves as the training set is more updated.