Chromosome analysis is an important procedure to detect genetic diseases. However, the process of identifying chromosomes can be very time consuming since they are selected manually. In an automated chromosome analysis system, the thresholding step is often used to identify individuals or cluster of chromosomes. This paper provides a review of threshold techniques in literature applied to chromosome segmentation. We have implemented fifteen methods, which are divided into four categories: clustering, entropy, histogram and local based methods. The techniques were evaluated with CRCN-NE and BioImLab datasets, consisting of 112 images, using the metrics of accuracy, precision, MCC and sensitivity. Results have showed that clustering algorithms achieved better results for both bases. A post processing step was also applied, increasing the quality of results. We could observe that the image acquisition process has a significant impact on the algorithm choice.