diff --git a/README.md b/README.md
index dd9a8c1..3d8798b 100644
--- a/README.md
+++ b/README.md
@@ -115,10 +115,18 @@ On each Rhasspy, in Rhasspy - Settings - Wake Word, set `Hermes MQTT`, like
 
 ### openWakeWord
 
-openWakeWord listens for all wake-words like "alexa", "hey mycroft", "hey jarvis", and [others](https://github.com/dscripka/openWakeWord#pre-trained-models). These settings ensure Rhasspy is only activated once per wake-word, and help reduce false activations.
+openWakeWord listens for wake-words like "alexa", "hey mycroft", "hey jarvis", and [others](https://github.com/dscripka/openWakeWord#pre-trained-models). Use `model_names` to specify which wake-words to listen for. (See [Pre-Trained Models](https://github.com/dscripka/openWakeWord#pre-trained-models) documentation, and which [`model_names`](https://github.com/dscripka/openWakeWord/blob/main/openwakeword/__init__.py) to use.)
+
+Delete any wake-words that you don't want to activate on. Or remove the entire `model_names` section to use all pre-trained models.
 
 ```yaml
 oww:
+  model_names:  # From https://github.com/dscripka/openWakeWord/blob/main/openwakeword/__init__.py
+    - alexa  # Delete to ignore this wake-word
+    - hey_mycroft
+    - hey_jarvis
+    - timer
+    - weather
   activation_samples: 3  # Number of samples in moving average
   activation_threshold: 0.7  # Trigger wakeword when average above this threshold
   deactivation_threshold: 0.2  # Do not trigger again until average falls below this threshold
@@ -126,10 +134,11 @@ oww:
   vad_threshold: 0.5
   enable_speex_noise_suppression: false
 ```
+The other `oww` settings ensure Rhasspy is only activated once per wake-word, and help reduce false activations. 
 
 In the example above, the latest 3 audio samples received over UDP are averaged together, and if the average confidence that a wake-word has been spoken is above 0.7 (70%), then Rhasspy is notified. Rhasspy will not be notified again until the average confidence drops below 0.2 (20%), i.e. the wake-word has ended.
 
-Settings for voice activity detection (VAD) and noise suppression are also provided. See openWakeWord's [Recommendations for Usage](https://github.com/dscripka/openWakeWord#recommendations-for-usage).
+Settings for voice activity detection (VAD) and noise suppression are also provided. (See openWakeWord's [Recommendations for Usage](https://github.com/dscripka/openWakeWord#recommendations-for-usage).)
 
 ## Contributing
 Feel free to open an Issue if you have a problem, need help or have an idea. PRs always welcome.
diff --git a/config.yaml.example b/config.yaml.example
index 5dc6081..b64412a 100644
--- a/config.yaml.example
+++ b/config.yaml.example
@@ -11,6 +11,12 @@ mqtt:
 
 # Open Wake Word config
 oww:
+  model_names:  # From https://github.com/dscripka/openWakeWord/blob/main/openwakeword/__init__.py
+    - alexa  # Delete to ignore this wake-word
+    - hey_mycroft
+    - hey_jarvis
+    - timer
+    - weather
   activation_samples: 3  # Number of samples in moving average
   activation_threshold: 0.7  # Trigger wakeword when average above this threshold
   deactivation_threshold: 0.2  # Do not trigger again until average falls below this threshold
diff --git a/detect.py b/detect.py
index 63018f3..1b08731 100644
--- a/detect.py
+++ b/detect.py
@@ -50,6 +50,7 @@ def load_config(config_file):
             "password": None,
         },
         "oww": {
+            "model_names": ["alexa", "hey_mycroft", "hey_jarvis", "timer", "weather"],
             "activation_threshold": 0.7,
             "deactivation_threshold": 0.2,
             "activation_samples": 3,
@@ -125,17 +126,21 @@ class Prediction(threading.Thread):
         )
 
     def run(self):
-        """Wake word detection thread."""
+        """
+        Wake word detection thread.
+
+        Detect and filter all wake-words, but only publish to MQTT if wake-word model name is listed
+        in config.yaml.
+        """
         while True:
             roomname, timestamp, audio = self.queue.get()
             prediction = self.oww.predict(audio)
             for wakeword in prediction.keys():
                 confidence = prediction[wakeword]
-                if self.__filter(wakeword, confidence):
-                    print(
-                        f"Detected wakeword {wakeword} in {roomname}",
-                        flush=True,
-                    )
+                if (
+                    self.__filter(wakeword, confidence)
+                    and wakeword in config["oww"]["model_names"]
+                ):
                     self.__publish(wakeword, roomname)
 
     def __filter(self, wakeword, confidence):
@@ -188,7 +193,10 @@ class Prediction(threading.Thread):
             "customEntities": None,
         }
         self.mqtt.publish(f"hermes/hotword/{wakeword}/detected", dumps(payload))
-        print("MQTT: Published to Rhasspy", flush=True)
+        print(
+            "MQTT: Published wakeword {wakeword}, siteId {roomname} to Rhasspy",
+            flush=True,
+        )
 
 
 if __name__ == "__main__":