This paper presents our results obtained by experimenting with different classifiers and different types of features to combine different speech recognizers in order to increase isolated speech commands recognition accuracy. Two different speech corpora were used to evaluate classifiers and features performance: “Asmens kodai” – person code which consists of 11 digits, “Skaiciai” – digits from 0 to 9. Compared suggested speech recognizers combination method with best single recognizer accuracy increased based on corpora: “Asmens kodai” – 0.352 % and “Skaiciai” – 1.057 %. Compared with combination that was made using voting method accuracy increased based on corpora: “Asmens kodai” – 18.7 % and “Skaiciai” – 2.9 %. Classifiers that were evaluated in different speech recognizers combination task: Naïve Bayes, Random forest, Nearest neighbors, CART and Support vector classifiers. Highest accuracy increase obtained by using Random forest classifier.