The deaf community relies on sign language as the primary means of communication. For the millions of people around the world who suffer from hearing loss, interaction with hearing people is quite difficult. The main objective of Sign language recognition (SLR) is the development of automatic SLR systems to facilitate communication with the deaf community. Arabic SLR (ArSLR) specifically did not receive much attention until recent years. This work presents a comprehensive comparison between two different recognition techniques for continuous ArSLR, namely a Modified k-Nearest Neighbor (MKNN) which is suitable for sequential data and Hidden Markov Models (HMMs) techniques based on two different toolkits. Additionally, in this work, two new ArSL datasets composed of 40 Arabic sentences are collected using Polhemus G4 motion tracker and a camera. An existing glove-based dataset is employed in this work as well. The three datasets are made publicly available to the research community. The advantages and disadvantages of each data acquisition approach and classification technique are discussed in this paper. In the experimental results section, it is shown that classification accuracy for sign sentences acquired using a motion tracker are very similar the classification accuracy for sentences acquired using sensor gloves. The modified KNN solution is inferior to HMMs in terms of the computational time required for classification.