Hearing impaired individuals can easily overcome the barriers in communicating with other members of the society via computer technology. In this study, the recognition of dynamic words in Turkish Sign Language (TSL) with two hands was studied using the Leap Motion Controller (LMC) device. 50 dynamic words were determined considering the similarities and differences among themselves, and a dataset was created using 4 signers. For the system proposed in this paper, a comprehensive feature extraction process is executed. Applying feature selection algorithms and PCA, LDA and PCA+LDA dimension reduction methods to this dataset, new datasets with less dimension were obtained. For the first time, ELM architectures were operated as a classifier in a sign language recognition system. Recognition performance was tested with 5 different ELM networks and 2 classical classifiers and the results were compared. In addition, comparisons of classical and ELM based classifiers were presented. The 10-fold cross validation method was used to test the validity of the proposed system and the accuracy of the results obtained. Based on the results obtained by a comprehensive analysis, it was observed that the ML-KELM classifier maintains its performance rate and gives the highest performance rate. At the same time, it has been observed that ML-KELM classifier has a stable structure, which offers less user intervention.